分类：复杂网络代写complex networks代考

计算机代写|复杂网络代写complex network代考|Results

Posted on 2023年5月16日2023年5月16日 by statistics-lab_01, statistics-lab_01

如果你也在怎样代写复杂网络complex network这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

在网络理论的背景下，复杂网络是具有非微观拓扑特征的图（网络）这些特征在格子或随机图等简单网络中不出现，但在代表真实系统的网络中经常出现。

statistics-lab™ 为您的留学生涯保驾护航在代写复杂网络complex network方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写复杂网络complex network代写方面经验极为丰富，各种代写复杂网络complex network相关的作业也就用不着说。

我们提供的复杂网络complex network及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

计算机代写|复杂网络代写complex network代考|Open Positions, Teams, and Candidate Pool

计算机代写|复杂网络代写complex network代考|Results

Network of words. Only about $7 \%$ (484 out of 7328$)$ of all papers are TwMLrelated. Previous studies have empirically observed that complex methods such as knowledge graphs or high-dimensional numeric embeddings are less reliable for characterizing rare concepts or terms $[15,29]$. Because of this rarity issue of TwML papers, we use a word co-occurrence network in place of more sophisticated methods. The resulting network contains 10,698 nodes and 254,347 edges.
The community detection algorithm generated 25 communities, with a modularity score of 0.33 . As given in Table 1, TwML-related words are concentrated in two communities. Among them, seven words that are mostly related to Differential Privacy (DP) separate from the rest into one community (second row in Table 1). Another community of 1127 words contains 26 other TwML-specific words. For convenience we shall refer to these communities as DP and nonDP community, respectively. The remaining 8 TwML words – which are mostly ambiguous such as ‘metric’ or ‘procedur’ or general such as ‘trustworthi’-get distributed across 6 communities.

Figure 2 visualizes the overall network, focusing on the two TwML-specific communities. We categorize the TwML words into four subject-based categories:

Privacy: ‘privaci’, ‘differenti’, ‘privat’, ‘guarantee’, ‘concern’,’preserv’,
Interpretability: ‘transpar’,’interpret’,’account’,
General: ‘trustworthi’, ‘mechan’,’algorithm’,’data’,
Fairness: all others.
From the relative position of words in each category in Fig. 2, it is evident that a number of privacy-specific and fairness-specific words cluster together, and these two clusters are well-separated from each other.

计算机代写|复杂网络代写complex network代考|Discussion

A number of interesting insights come out from the above analysis.
Network of Words. The differential distribution of TwML words within communities, as observed in Table 1, indicates that TwML papers tend to focus more on certain lines of research, methods or applications than others. In the context of ML bias and fairness, this is echoed by the review article of [16]. They observed that addressing group fairness in classification problems has received disproportionately high interest compared to other fairness categories (e.g. individual fairness, subgroup fairness) and types of methods (e.g. clustering, graph embedding); see Table 7 therein. Within the TwML words, Differential Privacy (DP)-specific words and those related to fairness and transparency group separately into two different communities. A potential reason for this may be that DP is a comparatively older research area, and has seen more theoretical developments than relatively new topics like fairness or transparency.

Paper-level Fingerprinting. All papers in Table 3 with high relevance scores are on comparatively complex algorithms. A number of these areas have been heavily researched of late, such as reinforcement learning (RL; papers 1,14,19), bandit problems $(2,4,18)$, anomaly detection $(2,9,10,11)$, representation learning $(11,13,15)$, multitask problems $(2,8,10,13)$, dirichlet process $(3,22)$, and nonconvex optimization $(24,25)$.

复杂网络代写

计算机代写|复杂网络代写complex network代考|Results

词语网络。所有论文中只有约7%(7328篇中的484篇)与twml相关。以前的研究已经通过经验观察到，复杂的方法，如知识图或高维数字嵌入，在描述罕见概念或术语方面不太可靠[15,29]。由于TwML论文的稀缺性问题，我们使用单词共现网络来代替更复杂的方法。最终的网络包含10698个节点和254347条边。
社区检测算法生成了25个社区，模块化得分为0.33。如表1所示，与twml相关的词集中在两个社区。其中，与差分隐私(DP)最相关的7个单词与其他单词分离成一个社区(表1第二行)。另一个包含1127个单词的社区包含26个其他twml特定单词。为方便起见，我们将这些社区分别称为DP和nonDP社区。剩下的8个TwML单词——大多是模棱两可的，如“度量”或“程序”，或一般的，如“值得信赖”——分布在6个社区。

图2显示了整个网络，重点关注两个特定于twml的社区。我们将TwML单词分为四个基于主题的类别:

隐私:’privaci’， ‘different ‘，’ privaci’， ‘guarantee’， ‘concern’，’ preserve ‘，

可解释性:“transpar”、“解释”、“账户”,

通用:“值得信赖”、“机制”、“算法”、“数据”、

公平:所有其他的。
从图2中每个类别中单词的相对位置可以明显看出，许多特定于隐私和特定于公平的单词聚在一起，并且这两个聚类彼此分离得很好。

计算机代写|复杂网络代写complex network代考|Discussion

从上述分析中得出了一些有趣的见解。
词语网络。如表1所示，社区内TwML单词的差异分布表明，TwML论文往往更关注某些研究、方法或应用领域。在机器学习偏见和公平性的背景下，[16]的综述文章也回应了这一点。他们观察到，与其他公平类别(如个人公平、子群体公平)和方法类型(如聚类、图嵌入)相比，在分类问题中解决群体公平问题获得了不成比例的高兴趣;见其中表7。在TwML单词中，特定于差分隐私(DP)的单词和与公平和透明度相关的单词分别分为两个不同的社区。造成这种情况的一个潜在原因可能是，DP是一个相对较老的研究领域，与公平或透明度等相对较新的主题相比，它已经看到了更多的理论发展。

Paper-level指纹。表3中相关度较高的论文都是关于比较复杂的算法。其中一些领域最近得到了大量的研究，比如强化学习(RL;论文1,14,19)，盗匪问题$(2,4,18)$，异常检测$(2,9,10,11)$，表示学习$(11,13,15)$，多任务问题$(2,8,10,13)$，dirichlet过程$(3,22)$和非凸优化$(24,25)$。

cs代写|复杂网络代写complex network代考请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题，以及设计新的和创新的金融产品。

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

术语广义线性模型（GLM）通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归，以及方差分析和方差分析（仅含固定效应）。

有限元方法代写

有限元方法（FEM）是一种流行的方法，用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。

有限元是一种通用的数值方法，用于解决两个或三个空间变量的偏微分方程（即一些边界值问题）。为了解决一个问题，有限元将一个大系统细分为更小、更简单的部分，称为有限元。这是通过在空间维度上的特定空间离散化来实现的，它是通过构建对象的网格来实现的：用于求解的数值域，它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统，以模拟整个问题。然后，有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

随机分析代写

随机微积分是数学的一个分支，对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。

时间序列分析代写

随机过程，是依赖于参数的一组随机变量的全体，参数通常是时间。随机变量是随机现象的数量表现，其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值（如1秒，5分钟，12小时，7天，1年），因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中，往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录，以得到其自身发展的规律。

回归分析代写

多元回归分析渐进（Multiple Regression Analysis Asymptotics）属于计量经济学领域，主要是一种数学上的统计分析方法，可以分析复杂情况下各影响因素的数学关系，在自然科学、社会和经济学等多个领域内应用广泛。

MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习和应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|复杂网络代写complex network代考|Open Positions, Teams, and Candidate Pool

Posted on 2023年5月16日2023年5月16日 by statistics-lab_01, statistics-lab_01

如果你也在怎样代写复杂网络complex network这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的复杂网络complex network及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

计算机代写|复杂网络代写complex network代考|Open Positions, Teams, and Candidate Pool

For each network, we run 100 trials. In each trial, we sample $10 \%, 20 \%$, and $30 \%$ of nodes randomly as open positions. To simulate the pool of candidates we consider two cases: (1) the candidate pool consists of the nodes set to open (with the same attributes), and (2) the candidate pool consists of two copies of each node set to open. The first setting corresponds to the case where a ‘batch’ of new employees has been hired, and now the employees need to be assigned to teams without considering the hiring process. The second setting corresponds to the case where we consider both hiring and assignment procedures. Moreover, the way we assign attributes to nodes ensures that changes in homophily are actually due to employee assignment, rather than changes in attributes.

The fitness function governs which candidates are suitable for which positions. For the first sets of experiments- the evaluation of FairEA- we consider two fitness functions. In $F_1$, candidates are qualified for four randomly selected positions with fitness equal to a random number in $(0,1)$. In $F_2$, candidates are fit for the four open positions closest to the position that the candidate had previously filled with fitness equal to a random number in $(0,1)$.

计算机代写|复杂网络代写complex network代考|Baseline Methods

We use three baseline methods: (1) Random, which randomly assigns qualified candidates to each open position; (2) The weighted Hungarian algorithm, where the input is a bipartite graph whose two sides correspond to open positions and candidates. An edge $\left(o_a, c_b\right)$ exists if $w_{a b}>0$, and the weight of this edge is the sum of $w_{a b}$ and the diversity score as described in Sect. 4; and (3) Optimization, which uses the IPOPT solver in the GEKKO optimization suite [1] for solving the optimization problem with the two goals of maximizing fitness and diversity. This is the simplified version of the problem where fitness is maximized, as described in Sect. 3.1 and diversity is optimized by decreasing the gap between number of neighbors from class $_i$ to number of neighbors from class $_j$ for each newly assigned position.

We report results using the following metrics:

The overall fit score is the sum of the fitness scores for each matching. Let $F S_h$ and $F S_l$ be overall fit score of the best and worst possible matching in terms of fitness of employees for the open positions respectively and $F S_a$ be the overall fit score of the network $G$ after assignment using desired method. Then we define Percentage Improvement in Fitness $=\frac{F S-F S_l}{F S_h-F S_l} \cdot 100$.
The diversity of the network is measured by the assortativity coefficient [17]. Let $A C_b$ be the assortativity coefficient of $G^{\prime}$, the subgraph of initial network $G$ consisting only of filled positions, and $A C_b$ be the (assortativity coefficient of the network $G$ after assignments are made. Then the Percentage Improvement in Assortativity $=\frac{\left|A C_b\right|-\left|A C_a\right|}{\left|A C_b\right|} \cdot 100$.
The fraction of minorities in team $i$ is $F M_i=\frac{\min \left(\left|c 1_i\right|, . .\left|c k_i\right|\right)}{\left|c 1_i\right|+\ldots\left|c k_i\right|}$ where $\left|c j_i\right|$ is the number of individuals from class $_j$ in team $i$. Isolation Score is the average fraction of minorities. Isolation Score $=\frac{1}{k} \cdot \sum_{1 \leq i \leq k} F M_i$.

复杂网络代写

计算机代写|复杂网络代写complex network代考|Open Positions, Teams, and Candidate Pool

对于每个网络，我们运行100次试验。在每次试验中，我们随机抽取$10 \%, 20 \%$和$30 \%$的节点作为开放位置。为了模拟候选池，我们考虑两种情况:(1)候选池由要打开的节点组成(具有相同的属性)，(2)候选池由每个要打开的节点集的两个副本组成。第一个设置对应于“一批”新员工被雇用的情况，现在需要将员工分配到团队中，而不考虑招聘流程。第二个设置对应于我们同时考虑雇用和分配程序的情况。此外，我们将属性分配给节点的方式确保了同质性的变化实际上是由于员工分配，而不是属性的变化。

适应度函数决定了哪个候选人适合哪个职位。对于第一组实验-公平评估-我们考虑两个适应度函数。在$F_1$中，候选人有资格获得四个随机选择的职位，适应度等于$(0,1)$中的一个随机数。在$F_2$中，候选人适合于最接近候选人之前所填补的职位的四个空缺职位，其适合度等于$(0,1)$中的一个随机数。

计算机代写|复杂网络代写complex network代考|Baseline Methods

我们使用三种基线方法:(1)随机，随机分配符合条件的候选人到每个空缺职位;(2)加权匈牙利算法，其中输入是一个二部图，其两边对应于开放位置和候选位置。如果$w_{a b}>0$存在一条边$\left(o_a, c_b\right)$，该边的权值为$w_{a b}$与第4节中描述的多样性得分之和;(3)优化，使用GEKKO优化套件[1]中的IPOPT求解器来解决以适应度最大化和多样性最大化为两个目标的优化问题。这是问题的简化版本，其中适应度最大化，如3.1节所述，并且通过减少每个新分配位置的$_i$类的邻居数量与$_j$类的邻居数量之间的差距来优化多样性。

我们使用以下指标报告结果:

总体适合度分数是每次匹配的适合度分数之和。设$F S_h$和$F S_l$分别为空缺职位的员工适合度的最佳和最差可能匹配的总体适合度得分，$F S_a$为使用期望方法分配后的网络$G$的总体适合度得分。然后我们定义健身百分比改善$=\frac{F S-F S_l}{F S_h-F S_l} \cdot 100$。

网络的多样性通过选型系数来衡量[17]。设$A C_b$为初始网络$G$只包含填充位置的子图$G^{\prime}$的选型系数，$A C_b$为分配后的网络$G$的选型系数。然后是分类性的百分比改进$=\frac{\left|A C_b\right|-\left|A C_a\right|}{\left|A C_b\right|} \cdot 100$。

团队$i$中少数族裔的比例为$F M_i=\frac{\min \left(\left|c 1_i\right|, . .\left|c k_i\right|\right)}{\left|c 1_i\right|+\ldots\left|c k_i\right|}$，其中$\left|c j_i\right|$是团队$i$中来自班级$_j$的个人人数。隔离分数是少数民族的平均分数。隔离评分$=\frac{1}{k} \cdot \sum_{1 \leq i \leq k} F M_i$。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|复杂网络代写complex network代考|On Measuring the Diversity of Organizational Networks

Posted on 2023年5月16日2023年5月16日 by statistics-lab_01, statistics-lab_01

如果你也在怎样代写复杂网络complex network这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的复杂网络complex network及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

计算机代写|复杂网络代写complex network代考|On Measuring the Diversity of Organizational Networks

In order for commercial and non-profit organizations to succeed, it is important for those organizations to recruit a workforce that is not only skilled, but also diverse, as diversity has been positively associated with performance [15]. However, diversity cannot be measured only in terms of numbers: it is known that negative effects may happen when a network is structured in a way that resources are not accessible through the social capital accessible to members of a minority group [9]. Social capital consists of bridging resources from outside of an individual’s group (inter-group connections) and bonding resources from internal group connections (intra-group connections) [13].

The literature contains a number of metrics for measuring network diversity/segregation, the most prominent being assortativity [17]. However, when dealing with dynamic networks where new nodes are being added, it is useful to know not only what the diversity of a specific network snapshot is after those nodes are added, but how good it could have been. In other words, if new nodes join a network, what is the best assortativity that one could possibly achieve, given pre-existing structure of the network and restrictions on where the new nodes can join?

Our work is motivated by the example of an organization that is evaluating their hiring and employee assignment practices with respect to the diversity (gender, race, etc.) of the organizational network. When positions are open, some set of candidates apply for those positions. Each candidate has some amount (possibly zero) of suitability for each of the open positions. If one’s goal is to minimize segregation in the network while ensuring that each position is filled by a candidate who is suitable for that position, which candidate should one hire for each position? If there are significant gender disparities in applications across job categories (e.g., if software engineer candidates are disproportionately male), then it may not be possible to achieve perfect diversity in hiring and assignment; but nonetheless, it is useful to know how well one can do. One can imagine similar examples for, say, new graduate students joining an existing scientific collaboration network.

There has been a great deal of recent interest in fairness of hiring/assignment procedures (e.g., the Rooney Rule used by the American National Football League [5]). This is because one cannot simply eliminate an existing professional network and replace it with a diverse network; and moreover, at the hiring stage, the candidate pool may itself be non-diverse or exhibit correlations between protected attributes and skillsets.

计算机代写|复杂网络代写complex network代考|Related Work

The recent scientific literature contains many studies on modeling bias in human recruiting systems. For example, [20] examines strategies for hiring diverse faculty in universities, [2] shows the different likelihoods of hiring and promotion for candidates from different groups with equal skills, and [16] addresses the tradeoff between performance goals and company diversity. [15] shows a positive relationship between board member racial and gender diversity to performance of nonprofits. Unfortunately, automating recruiting systems will not necessarily solve problems of discrimination in hiring [8]. One solution is to make sure that protected attributes do not influence algorithmic decisions, but [7] shows that gender bias exists even after scrubbing gender indicators from a classifier. While traditional approaches measure diversity of organizations in terms of numbers [18], organizations are social networks, and network factors influence entrepreneurial success, mobility through occupational ladders, and access to employments [19].

Problem Formulation
We formulate this problem as a multi-objective problem in which the goal is to assign a set of newly-hired employees/employment candidates (without loss of generality, the ‘candidates’) to open positions so as to maximize (1) the fitness of employees to positions and (2) the diversity of the organizational network, under the constraint that all open positions must be filled. We compute diversity as assortativity, which measures the extent to which ‘like connect to like’. Figure 1 shows an overview of the problem.
The input for this problem consists of the following:
(1) An undirected network $G=(P, E)$, representing the professional network of an organization. Nodes represent positions ( $s$ filled, $m$ open). Each edge $\left(p_i, p_j\right)$ represents either a real or expected professional interaction between the employees who currently fill or will fill positions $p_i$ and $p_j$ (i.e., those employees do interact, or are expected to interact once the positions are filled).

(2) A set of $t$ candidates $(t \geq m$ ). If $t=m$, this problem represents the case where new employees have already been hired and need to be assigned e.g., newly hired software engineers are being assigned to teams. If $t>m$, this problem can be viewed as a combination of the hiring and assignment problems.
(3) The fitness of each candidate $c_j$ for each position $o_i$ (how well-qualified $c_j$ is for $o_i$ ). We assume that it is possible to match candidates to open positions such that each open position is filled subject to having at least one candidate with greater than zero fitness for each open position.
(4) An attribute of interest, such as gender, that divides employees/candidates into $k$ classes of attributes: class $_s, \ldots$, class $_k$. We assume that this attribute is categorical and each node can be member of just one class (e.g., minority and majority).

The output is a matching of candidates to open positions. We refer to the input and output in the rest of the paper as described in Table 1.

复杂网络代写

计算机代写|复杂网络代写complex network代考|On Measuring the Diversity of Organizational Networks

重点词汇
2667/5000
翻译
通用场景
为了使商业和非营利组织取得成功，对这些组织来说，重要的是招聘一支不仅技术熟练而且多样化的员工队伍，因为多样性与绩效呈正相关[15]。然而，多样性不能仅用数字来衡量:众所周知，当一个网络的结构方式使资源无法通过少数群体成员可以获得的社会资本获得时，可能会产生负面影响[9]。社会资本包括来自个人群体外部的桥接资源(群体间连接)和来自群体内部连接的粘合资源(群体内连接)[13]。

文献中包含了许多衡量网络多样性/隔离的指标，其中最突出的是分类性[17]。然而，在处理正在添加新节点的动态网络时，不仅要知道添加这些节点后特定网络快照的多样性，还要知道它本来可以有多好，这是很有用的。换句话说，如果新节点加入网络，在给定网络的现有结构和新节点可以加入的位置的限制下，人们可能实现的最佳分类是什么?

我们的工作是由一个组织的例子来激励的，该组织正在评估他们的招聘和员工分配实践，考虑到组织网络的多样性(性别、种族等)。当职位空缺时，一些候选人会申请这些职位。每个候选人对每个空缺职位都有一定程度(可能为零)的适合性。如果一个人的目标是尽量减少网络中的隔离，同时确保每个职位都由适合该职位的候选人填补，那么每个职位应该雇佣哪个候选人呢?如果在不同工作类别的申请中存在显著的性别差异(例如，如果软件工程师候选人是不成比例的男性)，那么在招聘和分配方面可能无法实现完美的多样性;但无论如何，知道一个人能做得多好是有用的。人们可以想象类似的例子，比如说，新的研究生加入一个现有的科学合作网络。

最近，人们对招聘/分配程序的公平性产生了极大的兴趣(例如，美国国家橄榄球联盟使用的鲁尼规则[5])。这是因为人们不能简单地消除现有的专业网络，并用一个多样化的网络取而代之;此外，在招聘阶段，候选人才库本身可能没有多样性，或者在受保护的属性和技能集之间表现出相关性。

计算机代写|复杂网络代写complex network代考|Related Work

最近的科学文献包含了许多关于人类招聘系统建模偏差的研究。例如，[20]研究了大学招聘多元化教师的策略，[2]显示了来自不同群体的具有相同技能的候选人的招聘和晋升的不同可能性，[16]解决了绩效目标和公司多样性之间的权衡。[15]显示了董事会成员种族和性别多样性对非营利组织绩效的正向关系。不幸的是，自动化招聘系统不一定能解决招聘中的歧视问题[8]。一种解决方案是确保受保护的属性不影响算法决策，但[7]表明，即使在从分类器中剔除性别指标后，性别偏见仍然存在。传统方法从数量上衡量组织的多样性[18]，但组织是社会网络，网络因素影响创业成功、通过职业阶梯的流动性和就业机会[19]。

问题表述
我们将这个问题表述为一个多目标问题，其目标是在所有空缺职位必须被填补的约束下，分配一组新雇佣的员工/就业候选人(不失去一般性，“候选人”)来开放职位，以最大限度地实现(1)员工对职位的适应性和(2)组织网络的多样性。我们将多样性作为分类性来计算，它衡量的是“相似连接到相似”的程度。图1显示了该问题的概述。
这个问题的输入包括以下内容:
(1)无向网络$G=(P, E)$，代表一个组织的专业网络。节点表示位置($s$填充，$m$打开)。每个边$\left(p_i, p_j\right)$代表当前填补或将填补职位$p_i$和$p_j$的员工之间的真实或预期的专业互动(即，这些员工进行互动，或预计一旦职位被填补)。

(2)一组$t$考生($(t \geq m$)。如果$t=m$，这个问题表示新员工已经被雇用，需要被分配的情况，例如，新雇用的软件工程师被分配到团队。如果$t>m$，这个问题可以看作是招聘和分配问题的结合。
(3)每个候选人$c_j$对每个职位的适合度$o_i$ ($c_j$对$o_i$有多适合)。我们假设有可能将候选人与空缺职位相匹配，这样每个空缺职位都有至少一个候选人对每个空缺职位具有大于零的适应度。
(4)一个感兴趣的属性，如性别，将员工/候选人划分为$k$属性类:类$_s, \ldots$，类$_k$。我们假设这个属性是分类的，每个节点只能是一个类的成员(例如，少数派和多数派)。

输出是空缺职位的候选人匹配。本文其余部分的输入和输出如表1所示。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|复杂网络代写complex network代考|TSKS33

Posted on 2023年2月17日2023年2月17日 by statistics-lab

如果你也在怎样代写复杂网络complex network这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的复杂网络complex network及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

计算机代写|复杂网络代写complex network代考|A Brief History of Network Thinking and Science

Mark Newman, Albert-László Barabási, and Duncan Watts (2006), all major contributors to “network science” (or the study of “complex networks”), ${ }^2$ trace its history to 1763 . That was when mathematician Leonard Euler studied a bridge problem in the city of Königsberg in Prussia (today’s Kaliningrad, an enclave of the Russian Federation on the Baltic Sea). ${ }^3$

A Google map of the central city of Kaliningrad is shown in Figure 3.1. The approximate positions of the seven bridges Euler studied when it was Königsberg are superimposed on the map to illustrate the mathematical problem Euler faced. Five of these bridges have been replaced by more recent structures, which are shown in the map. Two of the seven bridges do not exist today; they are the middle bridges connecting the island to the shores in Figure 3.1.

Euler’s problem was how to find a path that crossed the seven bridges that existed then, exactly once each. As the story goes, the people of Königsberg had tried to find such a path for a long time and failed. Euler proved mathematically that such a path did not exist. One might ask, what was the point in such a trivial finding? Newman, Barabási, and Watts (2006) remind us that Euler’s mathematical solution to the problem was the beginning of what is known today as the “graph theory.” A graph is a mathematical (abstract) object that consists of points (called “nodes” or “vertices”) and lines (called “ties,” “links,” or “edges”). Graph theory is the mathematical basis of “social network analysis,” which is a set of conceptual and mathematical tools that are used in analyzing and finding patterns in social networks (Wasserman \& Faust, 1994, pp. 92-94), such as governance networks.

To illustrate the concepts used in the above paragraph, let’s go back to a network map I introduced in Chapter 1: Figure 1.3b (reproduced here as Figure 3.1). Because it is the map of a special kind of network (two-mode network), there are two kinds of nodes (vertices) in it: circles and squares. The circles represent journal publications and the squares represent keywords. The lines connecting them are ties (edges). So, the map is an illustration of which journal articles are linked to which keywords (i.e., which journal publications used which keywords). In this network map, we can see how the keywords are grouped.

计算机代写|复杂网络代写complex network代考|Governance Networks

As the literature review in Chapter 1 shows, there were very few studies on governance networks in the 1990s, and the number of publications began to increase exponentially in the $2000 \mathrm{~s}$. The applications of governance networks in public administration practice predate the literature. Isett and her colleagues (2011) note that networks were being used in public service delivery as early as in the $1970 \mathrm{~s}$; their applications expanded in the 1980 s, during Reagan’s presidency; and they became more widespread in the 1990s, when the Clinton administration tried to shrink the size of the federal government and launched the “reinventing government” initiative.

Although the literature on governance networks has decades of history, there is no common conceptualization developed and used by the researchers. In their comprehensive review of the literature, Provan, Fish, and Sydow (2007) could not find an overarching conceptualization that would guide empirical studies of governance networks at that point. More than a decade after their review of the literature, no commonly accepted conceptualization, let alone a tightly formulated theory, of governance networks has emerged yet. The concept of “governance networks” is used usually in conjunction with, or interchangeably with, the terms “policy networks” and “public management networks.” Each of these three concepts has its conceptual history and there are differences, as well as similarities, among them. (For the different usages and meanings of the terms, see Compston, 2009, and Agranoff, 2007).

复杂网络代写

计算机代写|复杂网络代写complex network代考|A Brief History of Network Thinking and Science

Mark Newman、Albert-László Barabási 和 Duncan Watts (2006)，他们都是“网络科学”（或“复杂网络”研究）的主要贡献者，2其历史可追溯至1763年。那时，数学家伦纳德·欧拉 (Leonard Euler) 在普鲁士的柯尼斯堡市（今天的加里宁格勒，俄罗斯联邦在波罗的海的飞地）研究桥梁问题。3

加里宁格勒中心城市的谷歌地图如图 3.1 所示。地图上叠加了欧拉在柯尼斯堡时研究过的七座桥梁的大致位置，以说明欧拉面临的数学问题。其中五座桥梁已被更新的结构所取代，如地图所示。七座桥中有两座今天不存在了。它们是图 3.1 中连接岛屿和海岸的中间桥梁。

欧拉的问题是如何找到一条穿过当时存在的七座桥的路径，每座桥恰好一次。据说，柯尼斯堡的人们长期以来一直试图找到这样一条道路，但都失败了。欧拉用数学证明了这样一条路径是不存在的。有人可能会问，这样一个微不足道的发现有什么意义呢？Newman、Barabási 和 Watts (2006) 提醒我们，欧拉对该问题的数学解决方案是今天所谓的“图论”的开端。图是一种数学（抽象）对象，由点（称为“节点”或“顶点”）和线（称为“关系”、“链接”或“边”）组成。图论是“社交网络分析”的数学基础，“社交网络分析”是一组用于分析和寻找社交网络模式的概念和数学工具（Wasserman \& Faust，1994，pp.

为了说明上一段中使用的概念，让我们回到我在第一章中介绍的一张网络图：图1.3b（此处转载为图3.1）。因为它是一种特殊网络（双模网络）的地图，所以里面有两种节点（顶点）：圆和正方形。圆圈代表期刊出版物，方块代表关键词。连接它们的线是领带（边）。因此，该地图说明了哪些期刊文章链接到哪些关键字（即哪些期刊出版物使用了哪些关键字）。在此网络图中，我们可以看到关键字是如何分组的。

计算机代写|复杂网络代写complex network代考|Governance Networks

正如第 1 章的文献回顾所示，1990 年代关于治理网络的研究非常少，而到 20 世纪，出版物数量开始呈指数级增长2000 秒. 治理网络在公共行政实践中的应用早于文献。Isett 和她的同事 (2011) 指出，早在1970 秒; 在 80 年代里根担任总统期间，他们的申请得到了扩展；它们在 1990 年代变得更加普遍，当时克林顿政府试图缩小联邦政府的规模并发起“重塑政府”倡议。

尽管关于治理网络的文献已有数十年的历史，但研究人员并没有开发和使用共同的概念。在对文献的全面审查中，Provan、Fish 和 Sydow（2007 年）未能找到一个总体概念来指导当时治理网络的实证研究。在他们查阅文献十多年后，还没有出现普遍接受的治理网络概念，更不用说严格制定的治理网络理论了。“治理网络”的概念通常与“政策网络”和“公共管理网络”这两个术语结合使用或互换使用。这三个概念中的每一个都有其概念历史，它们之间既有区别也有相似之处。（对于术语的不同用法和含义，

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|复杂网络代写complex network代考|CS7280

Posted on 2023年2月17日2023年2月17日 by statistics-lab

如果你也在怎样代写复杂网络complex network这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的复杂网络complex network及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

计算机代写|复杂网络代写complex network代考|COVID-19 and Governance

The insight of governance theorists that multiple actors are involved in governance processes can be observed in the case of COVID-19 pandemic, particularly in the invention, manufacturing, and administration/delivery processes of COVID-19 vaccines. It would not be possible to describe the full process of and all the actors involved in vaccine development here (that would likely to take several other books), but a brief overview could help make the case that the vaccine development was a complex process in which multiple actors played roles. There were scientists, biotechnology companies, vaccine manufacturers, governments, national and international public health organizations (such as the US National Institutes of Health and the World Health Organization), parcel shipping companies (FedEx and UPS), medical doctors, local pharmacies, and others; they all played various roles in the vaccine development and delivery processes.

The US federal government, particularly its agency National Institutes of Health (NIH), played significant roles in the development of messenger RNA (mRNA) vaccines. The NIH scientists had been working on prototype coronavirus vaccines for years.

Once the DNA sequence of SARS-CoV-2 virus was published by the Chinese scientists in February 2020, more than 120 teams of academic scientists and commercial manufacturers around the world began collaborating with each other through information exchanges and sharing resources to develop vaccines (Pagliusi et al., 2020). Among them were the NIH scientists who collaborated with Moderna, a small vaccine research and development company, to produce one of the first mRNA vaccines.

The federal government financed Moderna’s research and development and manufacturing of the vaccines partly, and other parts came from Emory University, Vanderbilt University Medical Center, and the Dolly Parton COVID-19 Research Fund. ${ }^4$ In the meantime, two biotechnology companies Pfizer and BioNTech were collaborating to develop their vaccine based on the mRNA platform technology BioNTech scientists had been working for years. ${ }^5$ Behind these stories were thousands of scientists who discovered mRNA in the 1960s, developed its delivery methods in the 1970s, and tested the first mRNA flu vaccine in mice in the 1990s. ${ }^6$ The COVID19 pandemic accelerated this decades of work and brought them to the successful development and worldwide application of the mRNA vaccines.
The vaccine delivery and administration process also involved many organizations and individuals: vaccine manufacturers, governments, parcel shipping companies (FedEx and UPS), medical doctors, local pharmacies, and individuals who accepted or rejected to be vaccinated. It took major logistical operations by the US government agencies and parcel shipment companies to deliver the vaccines to local hospitals, pharmacies, and medical doctors’ offices.

计算机代写|复杂网络代写complex network代考|Governance Networks

The term governance is increasingly used together with the term networks, either as “governance networks” or “network governance.” In these usages, “governance” refers to the process of collective problem solving in human societies, and “governance networks” refers to the structural relations among the participants of this process.

We can observe various networks in our daily experiences. Think about electrical power grids, which connect powers stations to relay stations and to the users of electricity; water distributions networks, which connect pumping stations, reservoirs, and homes; transportation networks, which connect bus and train stations, and airports through roads, which are used by personal cars and taxis; and the citations networks among scholarly publications (Latora et al., 2017, p. xiv). Think also about social relationships: friendship networks, underground crime networks, professional networks, and business networks such as supply chains.

The social media platforms like Facebook, Twitter, and LinkedIn are very large networks. They link individuals to each other as “friends” and “followers” in a variety of ways. They allow network participants to “like” each other’s posts or “retweet” them. These personal links, likes, and retweets can be analyzed to find patterns in relationships using powerful algorithms. Then these patterns can be used to “suggest” new friends to network participants. The patterns can also be used to “target” specific individuals and groups to market products they are likely to purchase. The products may be merchandises, like dishwashers, and services, like vacation rentals. The products may also be political, ideological, or cultural messages, including conspiracy theories, like Q Anon.

The “Internet of Things” is the largest network of them all. IBM’s blog defines it as follows.

In a nutshell, the Internet of Things [IoT] is the concept of connecting any device (so long as it has an on/off switch) to the Internet and to other connected devices. The IoT is a giant network of connected things and people-all of which collect and share data about the way they are used and about the environment around them.

复杂网络代写

计算机代写|复杂网络代写complex network代考|COVID-19 and Governance

在 COVID-19 大流行的情况下，特别是在 COVID-19 疫苗的发明、制造和管理/交付过程中，可以观察到治理理论家的洞察力，即多个参与者参与治理过程。不可能在这里描述疫苗开发的完整过程和所有参与者（这可能需要其他几本书），但简要概述可能有助于证明疫苗开发是一个复杂的过程，其中多个演员扮演角色。有科学家、生物技术公司、疫苗制造商、政府、国家和国际公共卫生组织（如美国国立卫生研究院和世界卫生组织）、包裹运输公司（联邦快递和 UPS）、医生、当地药店和其他的;

美国联邦政府，特别是其机构国立卫生研究院 (NIH)，在信使 RNA (mRNA) 疫苗的开发中发挥了重要作用。美国国立卫生研究院的科学家多年来一直致力于研究冠状病毒疫苗原型。

2020 年 2 月中国科学家公布 SARS-CoV-2 病毒的 DNA 序列后，全球 120 多个学术科学家和商业制造商团队开始通过信息交流和资源共享相互合作开发疫苗（Pagliusi等人，2020 年）。其中包括与小型疫苗研发公司 Moderna 合作生产首批 mRNA 疫苗之一的 NIH 科学家。

联邦政府部分资助Moderna的疫苗研发和制造，其他部分来自埃默里大学、范德比尔特大学医学中心和多莉帕顿COVID-19研究基金。4与此同时，辉瑞和 BioNTech 两家生物技术公司正在合作开发基于 BioNTech 科学家多年研究的 mRNA 平台技术的疫苗。5这些故事的背后是成千上万的科学家，他们在 1960 年代发现了 mRNA，在 1970 年代开发了它的递送方法，并在 1990 年代在老鼠身上测试了第一个 mRNA 流感疫苗。6COVID19 大流行加速了这几十年的工作，使他们成功开发了 mRNA 疫苗并在全球范围内应用。
疫苗的递送和管理过程还涉及许多组织和个人：疫苗制造商、政府、包裹运输公司（FedEx 和 UPS）、医生、当地药店以及接受或拒绝接种疫苗的个人。美国政府机构和包裹运输公司需要进行大量后勤操作，才能将疫苗运送到当地医院、药房和医生办公室。

计算机代写|复杂网络代写complex network代考|Governance Networks

治理一词越来越多地与网络一词一起使用，称为“治理网络”或“网络治理”。在这些用法中，“治理”是指人类社会集体解决问题的过程，“治理网络”是指这一过程参与者之间的结构关系。

我们可以在日常经验中观察到各种网络。想一想连接发电站、中继站和电力用户的电网；连接泵站、水库和家庭的配水网络；交通网络，通过私家车和出租车使用的道路连接公共汽车站、火车站和机场；以及学术出版物之间的引用网络（Latora 等人，2017 年，第 xiv 页）。还要考虑社会关系：友谊网络、地下犯罪网络、专业网络和供应链等商业网络。

Facebook、Twitter 和 LinkedIn 等社交媒体平台是非常大的网络。他们以各种方式将个人彼此联系为“朋友”和“追随者”。它们允许网络参与者“喜欢”彼此的帖子或“转发”它们。可以分析这些个人链接、点赞和转推，以使用强大的算法找到关系中的模式。然后，这些模式可用于向网络参与者“推荐”新朋友。这些模式还可以用于“定位”特定的个人和群体，以推销他们可能购买的产品。这些产品可能是商品，如洗碗机，也可能是服务，如度假租赁。这些产品也可能是政治、意识形态或文化信息，包括阴谋论，如 Q Anon。

“物联网”是其中最大的网络。IBM的博客是这样定义的。

简而言之，物联网 [IoT] 是将任何设备（只要它具有开/关开关）连接到互联网和其他连接设备的概念。物联网是一个由相互连接的事物和人组成的巨大网络——所有这些都收集和共享有关它们的使用方式和周围环境的数据。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

计算机代写|复杂网络代写complex network代考|CS60078

Posted on 2023年2月17日2023年2月17日 by statistics-lab

如果你也在怎样代写复杂网络complex network这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的复杂网络complex network及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

计算机代写|复杂网络代写complex network代考|A Brief History of Governance

In the previous chapter, we saw that the keywords “self-governance” and “governance networks” began to emerge in the literature that was included in the Web of Science in the 1990s and these concepts began to mature in the 2000s. The history of thinking on “governance” is longer than what the Web of Science database suggests.

Kettl (2002) notes that the term “governance” has been used by the French since the 14th century (p. 119) and that indirect governmental tools, such as governments relying on contractors – tools we recognize as tools of governance today – existed for millennia. The Roman legions relied on their defense contractors, for example. The scope and scale of government contracting increased in the American government toward the end of the 20th century, he observes. Meanwhile, similar developments were taking place in Europe, particularly Britain (Rhodes, 1997).

These developments motivated some American and European public administration scholars to rethink their understandings of how public administration works. Peters and Pierre (1998) observe that the participation of multiple actors in governance processes was already happening in the second half of the 20th century, and the US and European scholars were merely catching up to this reality in the last couple of decades of the century. ${ }^1$ There were even earlier conceptualizations of governance, such as Harlan Cleveland’s (1972). He argued that multiple horizontally related organizations always play roles in multilateral public decision-making and problem-solving.

The increased interest in governance in the academic literature is a result of a series of observations social theorists and public policy and administration researchers make: that societies have become multicentered in the later 20th century (Castells, 1996; Jessop, 1990) and that in today’s world no governmental or private actor has the capacity to solve the increasingly complex and dynamic problems of societies (Goldsmith \& Eggers, 2004; Kooiman, 1993).

According to Nye (2002), the electronic technology revolution of the late 20th century changed the way human societies organize to solve their problems. Centralized and bureaucratic organizational forms – particularly, the hierarchically ordered nation-state-were suitable for the problems of the earlier industrial revolutions, because industrial production required central control. The new technologies made central control more difficult and unnecessary. Consequently, the nation-state has become a mismatch to today’s problems: It is too big for some problems and too small for others. That is why state and local governments and local businesses have gained importance at subnational levels and supra-governmental entities, like the United Nations, World Bank, and the European Union, have become more important in dealing with larger and global problems. In this new world, distance is less of a problem and globalization is the norm.

计算机代写|复杂网络代写complex network代考|Conceptualizations of Governance

Provan and Kenis (2007) observe that in the public administration literature, governance refers narrowly to the funding and oversight roles of government agencies, especially regarding the activities of private organizations that have been contracted to provide public services (p. 230). The conceptualizations by Frederickson (2002), Salamon (2002), and Kettl (2002) corroborate this observation, but there are some openings to broader views of governance in them.

Frederickson’s (2002) “mismatch problem” for American public administration in the early 21 st century is illustrative of the reasoning behind the narrow conceptualizations of governance. Her argues that there is a mismatch between the social problems and opportunities on one hand and the “capacities of the governmental jurisdictions” on the other (p. 11). To cope with the mismatch, public administrators representing different jurisdictions form “formal and informal horizontal and vertical linkages and patterns of cooperation,” which Frederickson calls “administrative conjunction” (p. 11). The best examples of administrative conjunction can be found in American metropolitan areas, where professional public administrators who work for local governments, councils of government, regional special districts, and others cooperate with each other. This description of governance is an extension of the “intergovernmental relations” studies, which have a long tradition in American political science and public administration.

Salamon (2002) proposes a broader view of governance: Not only multiple governmental organizations work in conjunction, but also nonprofits and private corporations work with them in solving problems and delivering public services. He argues that in the second half of the 20th century the scope of government actions expanded and their forms had changed in the United States and other countries. Instead of delivering public services directly, governments began employing an array of “indirect tools”: loans, loan guarantees, grants, contracts, social regulation, economic regulation, insurance, tax expenditures, vouchers, and others. In doing so, they relied on third parties, nonprofits, and private corporations (banks, private hospitals, social service agencies, industrial corporations, universities, and others). As an example of these developments, he notes that in the fiscal year 1999 , only $28.1 \%$ of the US federal government’s public service delivery was through “direct government” (activities such as delivery of goods and services, income support, interests, and direct loans); the remainder of the activities were indirect.

复杂网络代写

计算机代写|复杂网络代写complex network代考|A Brief History of Governance

在上一章中，我们看到“自治”和“治理网络”等关键词在 1990 年代开始出现在 Web of Science 收录的文献中，这些概念在 2000 年代开始成熟。关于“治理”的思考历史比 Web of Science 数据库所显示的还要长。

Kettl（2002 年）指出，“治理”一词自 14 世纪以来就被法国人使用（第 119 页），并且间接的政府工具，例如依赖承包商的政府——我们今天认为是治理工具的工具——存在于千年。例如，罗马军团依赖于他们的国防承包商。他观察到，到 20 世纪末，美国政府的政府承包范围和规模有所增加。与此同时，类似的发展正在欧洲发生，尤其是英国（Rhodes，1997）。

这些发展促使一些美国和欧洲的公共行政学者重新思考他们对公共行政运作方式的理解。Peters 和 Pierre（1998）观察到，多方参与治理过程在 20 世纪下半叶就已经发生，而美国和欧洲的学者只是在本世纪的最后几十年才赶上这一现实.1甚至还有更早的治理概念，例如 Harlan Cleveland 的（1972）。他认为，多个横向相关的组织总是在多边公共决策和问题解决中发挥作用。

学术文献中对治理的兴趣增加是社会理论家和公共政策与行政研究人员所做的一系列观察的结果：社会在 20 世纪后期变得多中心化（Castells，1996 年；Jessop，1990 年），而在今天世界上没有任何政府或私人行为者有能力解决日益复杂和动态的社会问题（Goldsmith \& Eggers，2004 年；Kooiman，1993 年）。

根据 Nye (2002) 的说法，20 世纪后期的电子技术革命改变了人类社会组织解决问题的方式。集权和官僚的组织形式——尤其是等级森严的民族国家——适合解决早期工业革命的问题，因为工业生产需要中央控制。新技术使中央控制变得更加困难和不必要。因此，民族国家与今天的问题不匹配：它对某些问题太大而对其他问题又太小。这就是为什么州和地方政府以及地方企业在地方层面和超政府实体（如联合国、世界银行和欧盟）中变得越来越重要，在处理更大的全球性问题时变得更加重要。在这个新世界里，距离不再是问题，全球化成为常态。

计算机代写|复杂网络代写complex network代考|Conceptualizations of Governance

Provan 和 Kenis（2007 年）观察到，在公共行政文献中，治理狭义地指政府机构的资金和监督作用，特别是关于已签约提供公共服务的私人组织的活动（第 230 页）。Frederickson（2002 年）、Salamon（2002 年）和 Kettl（2002 年）的概念化证实了这一观察，但其中有一些更广泛的治理观点。

弗雷德里克森 (Frederickson) (2002) 针对 21 世纪初美国公共行政的“不匹配问题”说明了狭义的治理概念化背后的原因。她认为，一方面是社会问题和机遇与另一方面的“政府管辖权的能力”之间存在不匹配（第 11 页）。为了应对这种不匹配，代表不同司法管辖区的公共行政人员形成了“正式和非正式的横向和纵向联系以及合作模式”，弗雷德里克森称之为“行政联合”（第 11 页）。行政联合的最好例子可以在美国的大都市地区找到，在那里，为地方政府、政府委员会、区域特区等工作的专业公共行政人员相互合作。

Salamon (2002) 提出了更广泛的治理观点：不仅多个政府组织协同工作，而且非营利组织和私营公司也与它们合作解决问题和提供公共服务。他认为，在 20 世纪下半叶，美国和其他国家的政府行为范围扩大了，形式也发生了变化。政府不再直接提供公共服务，而是开始使用一系列“间接工具”：贷款、贷款担保、赠款、合同、社会监管、经济监管、保险、税收支出、代金券等。为此，他们依赖第三方、非营利组织和私营公司（银行、私立医院、社会服务机构、工业公司、大学等）。作为这些发展的一个例子，28.1%美国联邦政府的公共服务提供是通过“直接政府”（例如提供商品和服务、收入支持、利息和直接贷款等活动）；其余活动是间接的。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

cs代写|复杂网络代写complex network代考|TSKS33

Posted on 2022年9月1日2022年9月1日 by statistics-lab

如果你也在怎样代写复杂网络complex network这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的复杂网络complex network及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

cs代写|复杂网络代写complex network代考|EXTENSIONS AND APPLICATIONS OF CNSS

In the above sections, we have surveyed some recent developments in the analysis and synthesis of CNSs with switching topologies, mainly focusing on the synchronization and consensus behaviors and comparison to complex networks and MASs’ scenarios. The above survey is by no means complete. However, it depicts the whole general framework of coordination control for CNSs with dynamic communication networks and lays the fundamental basis for other exciting and yet critical issues concerning CNSs with switching topologies. These extensions still deserve further study, although a variety of efficient tools have been successfully developed to solve various challenging problems in those active research fields. Next, we elaborate on several state-of-the-art extensions and applications of CNSs with dynamic topologies.

Resilience analysis and control of complex cyber-physical networks. Most of the units in various network infrastructures are cyber-physical systems in the Internet of Things era. One of the essential and significant features of the cyber-physical system is integrating and interacting with its physical and cyber layers. As a new generation of CNS, the complex cyber-physical network has received drastic attention in recent years. Specifically, the CNSs’ paradigm provides an excellent way to model various large-scale crucial infrastructure systems, such as power grid systems, transportation systems, water supply networks, and many others [4]. These systems all capture the basic features that large numbers of interconnected individuals through wired or wireless communication links, and many essential functions of these large-scale infrastructure systems fall under the purview of coordination of CNSs. Disruption of these critical networked infrastructures could be a real-world effect across an entire country and even further, significantly impacting public health and safety and leading to massive economic losses. The alarming historical events urgently remind us to seek solutions for maintaining certain functionality of CNSs against malicious cyberattacks (i.e., resilience or cybersecurity). It is critically essential to exploit security threats during the initial design and development phase.

cs代写|复杂网络代写complex network代考|ALGEBRAIC GRAPH THEORY

Suppose a CNS consists of $N$ nodes (agents) which interact with each other through a communication or sensing network or a combination of both. It is natural to model the interactions among the $N$ nodes (agents) by undirected or directed graphs. Without loss generality, the $N$ nodes can be labeled as node $1, \ldots, N$. Let $\mathcal{V}={1, \cdots, N}$ be the set of nodes. Then the directed graph is described by $(\mathcal{V}, \mathcal{E})$, where the set of edges $\mathcal{E} \subseteq \mathcal{V} \times \mathcal{V}$ represent the interactions among the $N$ nodes. For notational simplicity, the graph $(\mathcal{V}, \mathcal{E})$ is denoted by $\mathcal{G}$. The edge $(j, i) \in \mathcal{E}$ if and only if node $i$ can receive the information from node $j$. When $(j, i) \in \mathcal{E}$, node $j$ is said to be a neighbor of node $i$. Denote by $\mathcal{N}i$ the set of neighbors of node $i$. If there exists a sequence of distinct nodes $i_1, \ldots, i_m$ such that $\left(i, i_1\right),\left(i_1, i_2\right), \ldots,\left(i{m-1}, i_m\right),\left(i_m, k\right) \in \mathcal{E}$, then it is said that node $i$ has a directed path to node $k$, or node $k$ is reachable from node $i . \mathcal{G}$ is strongly connected if each node has at least one directed path to any other nodes. More generally, if there exists a node, called the root, which has at least one directed path to any other nodes, $\mathcal{G}$ is said to contain a directed spanning tree. Denote by $a_{i j}$ the weight of the edge $(j, i), i, j=1, \ldots, N$. It is assumed throughout this book that $a_{i j} \geq 0$, where $a_{i j}>0$ if and only if $(j, i) \in \mathcal{E}$, and $a_{i j}=0$, otherwise. In addition, it is assumed in this book that $a_{i i}=0$, that is, self-loop is forbidden. $\mathcal{G}$ is called an undirected graph if $(i, j) \in \mathcal{E}$ whenever $(j, i) \in \mathcal{E}$ and $a_{i j}=a_{j i}$. An undirected graph is connected if there exists at least one undirected path between each pair of distinct nodes. For undirected graphs, the existence of an undirected spanning tree is equivalent to being connected. However, for directed graphs, the existence of a directed spanning tree is a weaker condition than being strongly connected. Please see Figure $2.1$ for a directed graph which is not strongly connected but contains a directed spanning tree.

复杂网络代写

cs代写|复杂网络代写complex network代考|EXTENSIONS AND APPLICATIONS OF CNSS

在上述部分中，我们回顾了具有切换拓扑的 CNS 分析和综合的一些最新进展，主要关注同步和共识行为以及与复杂网络和 MAS 场景的比较。上述调查并不完整。然而，它描述了具有动态通信网络的 CNS 协调控制的整个一般框架，并为其他有关具有交换拓扑的 CNS 的令人兴奋但关键的问题奠定了基础。这些扩展仍然值得进一步研究，尽管已经成功开发了各种有效的工具来解决这些活跃研究领域中的各种具有挑战性的问题。接下来，我们详细阐述了具有动态拓扑的 CNS 的几个最先进的扩展和应用。

复杂网络物理网络的弹性分析和控制。各种网络基础设施中的大部分单元都是物联网时代的信息物理系统。网络物理系统的基本和重要特征之一是与其物理层和网络层集成和交互。作为新一代的CNS，复杂的信息物理网络近年来受到了广泛关注。具体来说，CNS 的范式为模拟各种大型关键基础设施系统（如电网系统、交通系统、供水网络等）提供了一种极好的方法 [4]。这些系统都捕捉到大量通过有线或无线通信链路相互连接的个体的基本特征，这些大型基础设施系统的许多基本功能都属于 CNS 协调的范围。这些关键网络基础设施的中断可能会对整个国家产生现实影响，甚至会进一步严重影响公共健康和安全，并导致巨大的经济损失。令人震惊的历史事件紧急提醒我们寻求解决方案，以维护 CNS 的某些功能以抵御恶意网络攻击（即弹性或网络安全）。在初始设计和开发阶段利用安全威胁至关重要。严重影响公共健康和安全，并导致巨大的经济损失。令人震惊的历史事件紧急提醒我们寻求解决方案，以维护 CNS 的某些功能以抵御恶意网络攻击（即弹性或网络安全）。在初始设计和开发阶段利用安全威胁至关重要。严重影响公共健康和安全，并导致巨大的经济损失。令人震惊的历史事件紧急提醒我们寻求解决方案，以维护 CNS 的某些功能以抵御恶意网络攻击（即弹性或网络安全）。在初始设计和开发阶段利用安全威胁至关重要。

cs代写|复杂网络代写complex network代考|ALGEBRAIC GRAPH THEORY

假设一个 CNS 由ñ通过通信或传感网络或两者的组合相互交互的节点（代理）。模型之间的相互作用是很自然的ñ通过无向或有向图的节点（代理）。不失一般性，ñ节点可以标记为节点1,…,ñ. 让在=1,⋯,ñ是节点的集合。然后有向图描述为(在,和), 其中边的集合和⊆在×在表示之间的相互作用ñ节点。为符号简单起见，该图(在,和)表示为G. 边缘(j,一世)∈和当且仅当节点一世可以从节点接收信息j. 什么时候(j,一世)∈和, 节点j被称为节点的邻居一世. 表示为ñ一世节点的邻居集合一世. 如果存在一系列不同的节点一世1,…,一世米这样(一世,一世1),(一世1,一世2),…,(一世米−1,一世米),(一世米,ķ)∈和，则称该节点一世有一个指向节点的路径ķ, 或节点ķ可从节点访问一世.G如果每个节点至少有一条到任何其他节点的有向路径，则它是强连接的。更一般地说，如果存在一个称为根的节点，它至少有一条到任何其他节点的有向路径，G据说包含有向生成树。表示为一个一世j边缘的重量(j,一世),一世,j=1,…,ñ. 本书通篇假定一个一世j≥0，在哪里一个一世j>0当且仅当(j,一世)∈和，和一个一世j=0，否则。此外，本书假设一个一世一世=0，即禁止自循环。G称为无向图，如果(一世,j)∈和每当(j,一世)∈和和一个一世j=一个j一世. 如果每对不同的节点之间至少存在一条无向路径，则无向图是连通的。对于无向图，无向生成树的存在相当于是连通的。然而，对于有向图，有向生成树的存在是比强连接更弱的条件。请看图2.1对于没有强连接但包含有向生成树的有向图。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

cs代写|复杂网络代写complex network代考|CS7280

Posted on 2022年9月1日2022年9月1日 by statistics-lab

如果你也在怎样代写复杂网络complex network这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的复杂网络complex network及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

cs代写|复杂网络代写complex network代考|SYNCHRONIZATION OF COMPLEX NETWORKS

In the field of complex networks’ synchronization with switching topologies, a wide range of research has been recently focused on dealing with issues related to the switchings and their effects on synchronization.

There has been increasing recognition that each topology candidate’s properties and the switching strategy for topologies play essential roles in achieving synchronization for complex networks with switching topologies. The analytical approaches for synchronization of continuous- and discrete-time complex networks with switching topologies are generally different. Mathematically, the continuous-time complex network with switching topologies is a special kind of those with time-varying topology. However, it is preliminarily assumed in some existing works on synchronization of continuous-time network systems with time-varying topology that the connection links evolve continuously over time with a known bound for the changing rate [103] or with a time-varying Laplacian matrix being simultaneously diagonalizable [11]. Thus, the techniques developed in these works to solve synchronization problem of complex networks with special time-varying topology are generally hard to apply to that with switching topologies, especially to the case with directed switching topologies.

Specifically, averaging-based approaches were developed to analyze synchronization of continuous-time complex networks with fast switching topologies $[7,140]$ while multiple Lyapunov functions (MLFs)-based approaches were developed to analyze synchronization of continuous-time complex networks with slowly switching topologies (especially for the case with directed switching topologies) [190]. Furthermore, MLFLs-based approaches were usually employed to analyze synchronization of continuous-time complex networks with switching topologies under delayed or sampled-data coupling [90, 187]. Common Lyapunov function (CLF)- and functional (CLFL)-based approaches are applicable only to some special continuous-time complex networks with switching topologies such as each possible topology candidate is undirected [222].

cs代写|复杂网络代写complex network代考|CONSENSUS OF MASS WITH SWITCHING TOPOLOGIES

Since the pioneer works [65] in which heading consensus of the linearized Vicsek’s model was analyzed, consensus of MASs with switching topologies has attracted increasing attention from a wide range of scientific interests.

Consensus of first-order MASs with switching topologies: In the year of 2004, consensus problem of continuous time first-order (integrator-type) MASs with directed switching and balanced topology was formulated and studied in [116]. Due to the balanced property of each possible topology candidate, a common Lyapunov function was constructed in [116] for analyzing the convergence behaviors of disagreement vector. Consensus of both continuous- and discrete time first-order MASs with directed switching topologies was further studied in [128] where each possible topology candidate is not required to be balanced. By using graphical approaches, some interesting issues on consensus of a class of first-order MASs with switching topologies were further addressed in [13]. By employing a CLFL based approach, it was proven in [83] that average consensus in continuous time first-order MASs with time delayed protocol can be achieved if each topology candidate is strongly connected and balanced, and some linear matrix inequalities hold. Note that most of the aforementioned results are concerned with consensus of first-order MASs with deterministically switching topologies. However, considering the underlying topology may randomly switch among a set of topology candidates in some practical applications, there have been a number of results focusing on consensus of first-order MASs with randomly switching topologies $[54,155,156]$.

Consensus of second-order MASs with switching topologies: Based on the stability results for switched systems provided in [108], some dwell time (DT) based criteria for consensus of continuous time second-order MASs under directed switching topologies were established in [129] where it was revealed that consensus can be achieved if each topology candidate contains a directed spanning tree and the DT for switchings among different topology candidates is larger than a threshold value. When the graph describing the communication topology among followers is undirected, it was proven in [59] by constructing a CLF that leader-following consensus could be achieved if the topology jointly contains a directed spanning tree. Later, leader-following consensus problem of MASs with switching jointly reachable interconnection and transmission delays was solved in [234] by designing the switching laws among topology candidates, where the dynamics of the leader are described by first-order integrator.

复杂网络代写

cs代写|复杂网络代写complex network代考|SYNCHRONIZATION OF COMPLEX NETWORKS

在复杂网络与交换拓扑的同步领域，最近广泛的研究集中在处理与交换相关的问题及其对同步的影响。

人们越来越认识到，每个拓扑候选的属性和拓扑的交换策略在实现具有交换拓扑的复杂网络的同步方面起着至关重要的作用。具有交换拓扑的连续和离散时间复杂网络同步的分析方法通常是不同的。在数学上，具有交换拓扑的连续时间复杂网络是具有时变拓扑的一种特殊网络。然而，在一些现有的关于具有时变拓扑的连续时间网络系统同步的工作中，初步假设连接链路随着时间的推移以已知的变化率[103]或时变拉普拉斯矩阵不断演变同时可对角化[11]。因此，

具体来说，开发了基于平均的方法来分析具有快速切换拓扑的连续时间复杂网络的同步[7,140]同时开发了多个基于 Lyapunov 函数 (MLF) 的方法来分析具有缓慢切换拓扑的连续时间复杂网络的同步（特别是对于有向切换拓扑的情况）[190]。此外，基于 MLFL 的方法通常用于分析在延迟或采样数据耦合下具有切换拓扑的连续时间复杂网络的同步 [90, 187]。基于通用 Lyapunov 函数 (CLF) 和泛函 (CLFL) 的方法仅适用于一些具有切换拓扑的特殊连续时间复杂网络，例如每个可能的拓扑候选者都是无向的 [222]。

cs代写|复杂网络代写complex network代考|CONSENSUS OF MASS WITH SWITCHING TOPOLOGIES

自从分析了线性化 Vicsek 模型的标题一致性的先驱工作 [65] 以来，具有切换拓扑的 MAS 的一致性越来越受到广泛科学兴趣的关注。

具有切换拓扑的一阶 MAS 的共识：2004 年，在 [116] 中制定和研究了具有定向切换和平衡拓扑的连续时间一阶（积分器型）MAS 的共识问题。由于每个可能的拓扑候选者的平衡特性，在[116]中构建了一个通用的 Lyapunov 函数来分析分歧向量的收敛行为。在 [128] 中进一步研究了具有定向开关拓扑的连续和离散时间一阶 MAS 的共识，其中不需要平衡每个可能的拓扑候选。通过使用图形方法，在 [13] 中进一步解决了关于具有切换拓扑的一类一阶 MAS 共识的一些有趣问题。通过采用基于 CLFL 的方法，在 [83] 中证明，如果每个拓扑候选者都是强连接和平衡的，并且一些线性矩阵不等式成立，则可以在具有时间延迟协议的连续时间一阶 MAS 中实现平均共识。请注意，上述大多数结果都与具有确定性切换拓扑的一阶 MAS 的共识有关。然而，考虑到在一些实际应用中，底层拓扑可能在一组候选拓扑之间随机切换，已经有许多结果集中在随机切换拓扑的一阶 MAS 的一致性上。请注意，上述大多数结果都与具有确定性切换拓扑的一阶 MAS 的共识有关。然而，考虑到在一些实际应用中，底层拓扑可能在一组候选拓扑之间随机切换，已经有许多结果集中在随机切换拓扑的一阶 MAS 的一致性上。请注意，上述大多数结果都与具有确定性切换拓扑的一阶 MAS 的共识有关。然而，考虑到在一些实际应用中，底层拓扑可能在一组候选拓扑之间随机切换，已经有许多结果集中在随机切换拓扑的一阶 MAS 的一致性上。[54,155,156].

具有切换拓扑的二阶 MAS 的共识：基于 [108] 中提供的切换系统的稳定性结果，在 [129 ] 其中表明，如果每个拓扑候选者包含有向生成树，并且用于在不同拓扑候选者之间切换的 DT 大于阈值，则可以达成共识。当描述跟随者之间的通信拓扑的图是无向的时，在[59]中通过构造一个 CLF 证明，如果拓扑共同包含有向生成树，则可以实现领导者跟随共识。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

cs代写|复杂网络代写complex network代考|CS60078

Posted on 2022年9月1日2022年9月1日 by statistics-lab

如果你也在怎样代写复杂网络complex network这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的复杂网络complex network及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

cs代写|复杂网络代写complex network代考|COMPLEX NETWORK SYSTEMS

Far from being separate entities, many natural, social, and engineering systems can be considered as CNSs associated with tight interactions among neighboring entities within them $[3,10,18,29,39,50,92,120,130,141,175,194,201,211,219]$. Roughly speaking, a CNS refers to a networking system that consists of lots of interconnected agents, where each agent is an elementary element or a fundamental unit with detailed contents depending on the nature of the specific network under consideration [175]. For example, the Internet is a CNS of routers and computers connected by various physical or wireless links. The cell can be described by a CNS of chemicals connected by chemical interactions. The scientific citation network is a CNS of papers and books linked by citations among them. The WeChat social network is a CNS whose agents are users and whose edges represent the relationships among users, to name just a few.

With the aid of coordination with neighboring individuals, a CNS can exhibit fascinating cooperative behaviors far beyond the individuals’ inherent properties. Prototypical cooperative behaviors include synchronization $[38,95,101,177]$, consensus $[76,118,128]$, swarming $[48,115]$, flocking $[117,161]$. In this book, we focus on the CNSs which include complex networks and MASs as special cases. A lot of new research challenges have been raised about understanding the emergence mechanisms responsible for various collective behaviors as well as global statistical properties of CNSs $[3,15,114,178]$. Network science, as a strong interdisciplinary research field, has been established at the first several years of the 21st century [110]. It is increasingly recognized that a detailed study on cooperative dynamics of CNSs would not only help researchers understand the evolution mechanism for macroscopical cooperative behaviors, but also prompt the application of network science to solve various engineering problems, e.g., design of distributed sensor networks [135], formation control of multiple unmanned aerial vehicles [37], distributed localization [89], and load assignment of multiple energy storage units in modern power grid [191].

Among the various cooperative behaviors of CNSs, synchronization of complex networks and consensus of MASs are the most fundamental yet most important ones. Synchronization of complex networks exhibits the cooperative behavior that the states of all entities within these networks achieve an agreement on some quantities of interest. Compared with stability analysis of an isolated control plant, synchronization behavior analysis in CNSs are much more challenging as the synchronization process is determined by the evolution of network topology as well as the inherent dynamics of individual units within these network systems $[96,102,121,198,199]$. As a topic closely related to synchronization of complex networks, the consensus of MASs has recently gained much attention from various research fields, especially the system science, control theory, and electrical engineering communities $[22,65,88,116,128]$. In the remainder of this chapter, we will review some existing results on achieving synchronization of complex networks and consensus of MASs over dynamically changing communication topologies.

cs代写|复杂网络代写complex network代考|DEFINITIONS OF SYNCHRONIZATION AND CONSENSUS

Before moving forward, the definition of consensus of MASs is given. Moreover, the synchronization of complex networks can be defined similarly.

Consider an MAS which consists of $N$ agents. Without loss of generality, we label the $N$ agents as agents $1, \ldots, N$, respectively. The dynamics of agent $i, i=1, \ldots, N$, are represented by
$$
\dot{x}_i(t)=f\left(t, x_i(t), u_i(t)\right),
$$
where $x_i(t) \in \mathbb{R}^n$ and $u_i(t) \in \mathbb{R}^m$ represent, respectively, the state and the control input, $f(\cdot, \cdot \cdot):\left[t_0,+\infty\right) \times \mathbb{R}^n \times \mathbb{R}^m \mapsto \mathbb{R}^n$ represents the nonlinear dynamics of agent i. A particular case is the general linear time-invariant MASs with the dynamics of agent $i$ are described by
$$
\dot{x}_i(t)=A x_i(t)+B u_i(t), i=1, \ldots, N,
$$
where $A \in \mathbb{R}^{n \times n}$ and $B \in \mathbb{R}^{n \times m}$ represent, respectively, the state matrix and control input matrix. For convenience, throughout this book, we call MAS (1.1) to represent the MAS whose dynamics are described by (1.1).

Definition $1.1$ Consensus of the MAS (1.1) is said to be achieved if for arbitrary initial conditions $x_i\left(t_0\right), i=1, \ldots, N$,
$$
\lim _{t \rightarrow \infty}\left|x_i(t)-x_j(t)\right|=0, i, j=1, \ldots, N .
$$
The definition of consensus for MAS (1.1) given by Eq. (1.3) does not concern about the final consensus states. However, it is sometimes important to make the states of all agents in the considered MASs to finally converge to some predesigned trajectory, especially from the viewpoint of controlling various complex engineering systems. To ensure the states of all agents in MAS (1.1) converge to some desired states, a target system (may be virtual) is introduced to the network (1.1) as
$$
\dot{s}(t)=f(t, s(t))
$$
for some given initial value $s\left(t_0\right) \in \mathbb{R}^n$. Under this scenario, we call agent $i$ whose dynamics are described by (1.1) the follower $i, i=1, \ldots, N$, and call the agent whose dynamics are described by (1.4) the leader.

复杂网络代写

cs代写|复杂网络代写complex network代考|COMPLEX NETWORK SYSTEMS

许多自然、社会和工程系统远不是独立的实体，而是可以被视为与其中相邻实体之间紧密相互作用相关的 CNS[3,10,18,29,39,50,92,120,130,141,175,194,201,211,219]. 粗略地说，CNS 是指由许多相互连接的代理组成的网络系统，其中每个代理是一个基本元素或基本单元，其详细内容取决于所考虑的特定网络的性质[175]。例如，互联网是通过各种物理或无线链路连接的路由器和计算机的 CNS。细胞可以通过化学相互作用连接的化学中枢神经系统来描述。科学引文网络是论文和书籍之间通过引文链接的 CNS。微信社交网络是一个CNS，其代理是用户，其边代表用户之间的关系，仅举几例。

借助与邻近个体的协调，CNS 可以表现出远远超出个体固有属性的迷人合作行为。典型的合作行为包括同步[38,95,101,177]，共识[76,118,128], 蜂拥而至[48,115], 植绒[117,161]. 在本书中，我们重点关注包含复杂网络和 MAS 的 CNS 作为特例。关于理解负责各种集体行为的出现机制以及 CNS 的全局统计特性，已经提出了许多新的研究挑战[3,15,114,178]. 网络科学作为一个强大的跨学科研究领域，在 21 世纪头几年已经确立[110]。人们越来越认识到，对 CNS 合作动力学的详细研究不仅有助于研究人员了解宏观合作行为的演化机制，而且还促进网络科学应用于解决各种工程问题，例如分布式传感器网络的设计 [135 ]，多架无人机编队控制[37]，分布式定位[89]，现代电网中多个储能单元的负荷分配[191]。

在 CNS 的各种协作行为中，复杂网络的同步和 MAS 的共识是最基本也是最重要的。复杂网络的同步展示了这些网络中所有实体的状态就某些感兴趣的数量达成一致的合作行为。与孤立控制设备的稳定性分析相比，CNS 中的同步行为分析更具挑战性，因为同步过程取决于网络拓扑的演变以及这些网络系统中各个单元的固有动态[96,102,121,198,199]. 作为与复杂网络同步密切相关的课题，MASs 的共识最近引起了各个研究领域的广泛关注，尤其是系统科学、控制理论和电气工程界。[22,65,88,116,128]. 在本章的剩余部分，我们将回顾一些关于实现复杂网络同步和 MAS 在动态变化的通信拓扑上的共识的现有结果。

cs代写|复杂网络代写complex network代考|DEFINITIONS OF SYNCHRONIZATION AND CONSENSUS

在继续之前，给出了MAS共识的定义。此外，可以类似地定义复杂网络的同步。
考虑一个 MAS，它由 $N$ 代理。不失一般性，我们标记 $N$ 代理人作为代理人 $1, \ldots, N$ ，分别。代理的动态 $i, i=1, \ldots, N$, 表示为
$$
\dot{x}i(t)=f\left(t, x_i(t), u_i(t)\right), $$ 在哪里 $x_i(t) \in \mathbb{R}^n$ 和 $u_i(t) \in \mathbb{R}^m$ 分别表示状态和控制输入， $f(\cdot, \cdot):\left[t_0,+\infty\right) \times \mathbb{R}^n \times \mathbb{R}^m \mapsto \mathbb{R}^n$ 表示代理 $i$ 的非线性动力学。一个特例是具有代理动力学的一般线性时不变 MAS $i$ 被描述为 $$ \dot{x}_i(t)=A x_i(t)+B u_i(t), i=1, \ldots, N, $$ 在哪里 $A \in \mathbb{R}^{n \times n}$ 和 $B \in \mathbb{R}^{n \times m}$ 分别表示状态矩阵和控制输入矩阵。为方便起见，在整本书中，我们称 MAS (1.1) 来表示其动力学由 (1.1) 描述的 MAS。定义 $1.1$ 如果对于任意初始条件，则据说可以实现 MAS (1.1) 的共识 $x_i\left(t_0\right), i=1, \ldots, N$ ， $$ \lim {t \rightarrow \infty}\left|x_i(t)-x_j(t)\right|=0, i, j=1, \ldots, N .
$$
由方程式给出的 MAS (1.1) 共识的定义。(1.3) 不关心最终共识状态。然而，有时重要的是使所考虑的 MAS 中的所有代理的状态最终收敛到某个预先设计的轨迹，特别是从控制各种复杂工程系统的角度来看。为了确保 MAS (1.1) 中所有代理的状态收敛到一些期望的状态，将目标系统（可能是虚拟的) 引入网络 (1.1) 为
$$
\dot{s}(t)=f(t, s(t))
$$
对于一些给定的初始值 $s\left(t_0\right) \in \mathbb{R}^n$. 在这种情况下，我们称代理 $i$ 其动力学由 $(1.1)$ 跟随者描述 $i, i=1, \ldots, N$ ，并调用由 (1.4) 描述动态的智能体为领导者。

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金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

cs代写|复杂网络代写complex network代考|TSKS33

Posted on 2022年7月6日2022年7月6日 by statistics-lab

如果你也在怎样代写复杂网络complex network这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的复杂网络complex network及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(Generalized) Linear Models 广义线性模型
Statistical Machine Learning 统计机器学习
Longitudinal Data Analysis 纵向数据分析
Foundations of Data Science 数据科学基础

cs代写|复杂网络代写complex network代考|Multiple Lyapunov functions

To proceed, the notion of time dependent switching is introduced.
As a special kind of hybrid dynamic system, switched system has been studied for quite some time by researchers from applied mathematics, systems and control fields. Roughly speaking, a switched system is a dynamic system that consists of a number of subsystems and a switching rule that determines switches among these subsystems. Suppose the switched system is generated by the following family of subsystems
$$
\dot{x}(t)=f_{p}(t, x(t)), x(t) \in \mathbb{R}^{n}, p \in{1, \ldots, \kappa},
$$
together with a switching signal $\sigma(t):\left[t_{0},+\infty\right) \mapsto{1, \ldots, \kappa}$. Note that $\sigma(t)$ is a piecewise constant function that switches at the switching time instants $t_{1}, t_{2}, \ldots$, and is constant on the time interval $\left[t_{k}, t_{k+1}\right), k=0,1, \ldots$. In this book, we assume $\sigma(t)$ is right continuous, i.e., $\sigma(t)=\lim {\iota} t \sigma(\iota)$, and $\inf {k \in \mathbb{N}}\left(t_{k+1}-t_{k}\right) \geq \tau_{m}$ for some given positive scalar $\tau_{m}$ where inf represents the infimum. Please see Figure $2.2$ for an example. Thus the switched systems with time-dependent switching signal $\sigma(t)$ can be described by the equation
$$
\dot{x}(t)=f_{\sigma(t)}(t, x(t)) .
$$
According to Theorem 2.1, each subsystem has a unique solution over arbitrary interval $\left[t_{k}, t_{k+1}\right), k=0,1, \ldots$, with arbitrary initial value $x\left(t_{k}\right) \in \mathbb{R}^{n}$ if the function $f_{p}$, for each $p=1, \ldots, \kappa$, is globally Lipschitz in $x(t)$ uniformly over $t$. Thus the switched system (2.10) is well defined for arbitrary switching signal $\sigma(t)$ defined above and any given initial value $x\left(t_{0}\right) \in \mathbb{R}^{n}$. Throughout this chapter, we assume that such a globally Lipschitz condition holds for the subsystems, and thus the well-definedness of the switched system is guaranteed. We further assume that $f_{p}\left(t, \mathbf{0}{n}\right)=\mathbf{0}{n}$ for each $p=1, \ldots, \kappa$. Thus, the zero vector is an equilibrium point of the switched system (2.10). Next, some stability notions for the zero equilibrium point of switched systems are introduced.

cs代写|复杂网络代写complex network代考|CONSENSUS OF LINEAR CNSS WITH DIRECTED SWITCHING TOPOLOGIES

In the past decade, the consensus problem of general linear CNSs has received a lot of attention $[76,146,162,185,186,224]$. Specifically, the consensus problem of linear CNSs under a directed fixed communication topology has been addressed in $[76,224]$. In [162], the robust consensus of linear CNSs with additive perturbations of the transfer matrices of the nominal dynamics was studied. In [163] and a number of subsequent papers, the robust consensus was analyzed from the viewpoint of the $\mathcal{H}_{\infty}$ control theory. Among other relevant references, we mention [146] where, while assuming that the open loop systems are Lyapunov stable, the consensus problem of linear CNSs with undirected switching topologies has been investigated. In the situation where the CNS is equipped with a leader and the topology of the system belongs to the class of directed switching topologies, the consensus tracking problem has been studied in $[185,186]$. One feature of the results in these references is that the open loop agents’ dynamics do not have to be Lyapunov stable. Note that the presence of the leader in the CNSs considered in these references facilitate the derivations and the direct analyses of the consensus error system. However, when the open loop systems are not Lyapunov stable and/or there is no designated leader in the group, the consensus problem for linear CNSs with directed switching topologies remains challenging.

Motivated by the above discussion, this section aims to study the consensus problem for linear CNSs with directed switching topologies. Several aspects of the current study are worth mentioning. Firstly, some of the assumptions in the existing works are dismissed, e.g., the open loop dynamics of the agents do not have to be Lyapunov stable in this chapter. Furthermore, the CNSs under consideration are not required to have a leader. Compared with the consensus problems for linear CNSs with a designated leader, the point of difference here concerns the assumption on the system’s communication topology. In the previous work on the consensus tracking of linear CNSs such as [185], each possible augmented system graph was required to contain a directed spanning tree rooted at the leader. Compared with that work, the switching topologies in this section are allowed to have spanning trees rooted at different nodes. This is a significant relaxation of the previous conditions since it enables the system to be reconfigured if necessary (e.g., to allow different nodes to serve as the formation leader). This also has a potential to make the system more reliable.

复杂网络代写

cs代写|复杂网络代写complex network代考|Multiple Lyapunov functions

为了继续，引入时间相关切换的概念。
切换系统作为一种特殊的混合动力系统，已经被应用数学、系统和控制领域的研究人员研究了很长时间。粗略地说，切换系统是一个动态系统，由若干子系统和决定这些子系统之间切换的切换规则组成。假设切换系统由以下子系统族生成
$$
\dot{x}(t)=f_{p}(t, x(t)), x(t) \in \mathbb{R}^{n}, p \in 1, \ldots, \kappa
$$
连同一个开关信号 $\sigma(t):\left[t_{0},+\infty\right) \mapsto 1, \ldots, \kappa$. 注意 $\sigma(t)$ 是在切换时刻切换的分段常数函数 $t_{1}, t_{2}, \ldots$, 并且在时间间隔上是常数 $\left[t_{k}, t_{k+1}\right), k=0,1, \ldots$ 在本书中，我们假设 $\sigma(t)$ 是右连续的，即， $\sigma(t)=\lim \iota \sigma(\iota)$ ，和 $\inf k \in \mathbb{N}\left(t_{k+1}-t_{k}\right) \geq \tau_{m}$ 对于一些给定的正标量 $\tau_{m}$ 其中 $\inf$ 表示下确界。请看图 $2.2$ 例如。因此具有时间相关开关信号的开关系统 $\sigma(t)$ 可以用方程来描述
$$
\dot{x}(t)=f_{\sigma(t)}(t, x(t)) .
$$
根据定理 2.1，每个子系统在任意区间上都有唯一解 $\left[t_{k}, t_{k+1}\right), k=0,1, \ldots$, 具有任意初始值 $x\left(t_{k}\right) \in \mathbb{R}^{n}$ 如果号是很好定义的 $\sigma(t)$ 上面定义的和任何给定的初始值 $x\left(t_{0}\right) \in \mathbb{R}^{n}$. 在本章中，我们假设这样的全局 Lipschitz 条件适用于子系统，从而保证了切换系统的良好定义。我们进一步假设 $f_{p}(t, \mathbf{0} n)=\mathbf{0} n$ 对于每个 $p=1, \ldots, \kappa$. 因此，零向量是切换系统 (2.10) 的平衡点。接下来，介绍了切换系统零平衡点的一些稳定性概念。

cs代写|复杂网络代写complex network代考|CONSENSUS OF LINEAR CNSS WITH DIRECTED SWITCHING TOPOLOGIES

在过去十年中，一般线性中枢神经系统的共识问题受到了很多关注[76,146,162,185,186,224]. 具体来说，在有向固定通信拓扑下的线性 CNS 的共识问题已在[76,224]. 在 [162] 中，研究了线性 CNS 与标称动力学的传递矩阵的加性扰动的稳健共识。在 [163] 和随后的一些论文中，稳健的共识是从H∞控制理论。在其他相关参考文献中，我们提到了[146]，其中假设开环系统是 Lyapunov 稳定的，研究了具有无向切换拓扑的线性 CNS 的共识问题。在CNS配备leader且系统拓扑属于有向交换拓扑类的情况下，研究了共识跟踪问题[185,186]. 这些参考文献中结果的一个特点是开环代理的动力学不必是李雅普诺夫稳定的。请注意，在这些参考文献中考虑的 CNS 中领导者的存在促进了对共识错误系统的推导和直接分析。然而，当开环系统不是李雅普诺夫稳定和/或组中没有指定的领导者时，具有定向切换拓扑的线性 CNS 的共识问题仍然具有挑战性。

受上述讨论的启发，本节旨在研究具有定向切换拓扑的线性 CNS 的共识问题。当前研究的几个方面值得一提。首先，现有工作中的一些假设被驳回，例如，在本章中，代理的开环动力学不必是 Lyapunov 稳定的。此外，正在考虑的 CNS 不需要有领导者。与具有指定领导者的线性 CNS 的共识问题相比，这里的不同之处在于对系统通信拓扑的假设。在之前关于线性 CNS 一致性跟踪的工作（如 [185]）中，每个可能的增强系统图都需要包含一个以领导者为根的有向生成树。与那部作品相比，本节中的交换拓扑允许具有植根于不同节点的生成树。这是对先前条件的显着放宽，因为它使系统能够在必要时重新配置（例如，允许不同的节点充当编队领导者）。这也有可能使系统更可靠。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写