## 统计代写|网络分析代写Network Analysis代考|Graphical Analysis

statistics-lab™ 为您的留学生涯保驾护航 在代写网络分析Network Analysis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。我们的专家在代写网络分析Network Analysis代写方面经验极为丰富，各种代写网络分析Network Analysis相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

## 统计代写|网络分析代写Network Analysis代考|Bipartite Graphs of Host Communications

Host communications observed in network traffic of Internet links could be naturally modeled with a bipartite graph $\mathcal{G}=(\mathcal{A}, \mathcal{B}, \mathcal{E})$, where $\mathcal{A}$ and $\mathcal{B}$ are two disjoint vertex sets, and $\mathcal{E} \subseteq \mathcal{A} \times \mathcal{B}$ is the edge set [35]. Specifically, all the source IP addresses observed in network traffic from one single direction of an Internet link form the vertex set $\mathcal{A}$, while the vertex set $\mathcal{B}$ consists of all the destination addresses observed in the same traffic. Each of the edges, $e_k$ in $\mathcal{G}$ connects one vertex $a_i \in \mathcal{A}$ and another vertex $b_j \in \mathcal{B}$.

Figure 2.2 illustrates an example of a simple bipartite graph that shows data communications between six source IP addresses $\left(s_1-s_6\right)$ and four destination IP addresses $\left(d_1-d_4\right)$. Note that an Internet link carries network traffic from two directions, thus we separate network traffic based on traffic directions and use bipartite graphs to model network traffic from two directions separately.

To study the social-behavior similarity of end hosts in network traffic, we leverage one-mode projection graphs of bipartite graphs that are used to extract hidden information or relationships between nodes within the same vertex sets [35]. Figure 2.3[a] illustrates the one-mode projection of the bipartite graph on the vertex set of the six left-side nodes, i.e., the source hosts $\left(s_1-s_6\right)$ in Fig. 2.2, while Fig. 2.3 [b] is the onemode projection on the four destination hosts $d_1-d_4$ in Fig. 2.2. An edge connects two nodes in the one-mode projection if and only if both nodes have connections to at least one same node in the bipartite graph. Thus studying one-mode projection graphs could potentially reveal the similarity or dissimilarity of communication patterns and traffic behaviors for networked systems and Internet applications.

We start by focusing on each dimension of the four-feature space, srCIP, dstIP, srcPrt, or dstPrt, and extract significant clusters of interest along this dimension. The extracted srCIP and dstIP clusters yield a set of interesting host behaviors (communication patterns), while the srcPrt and dstPrt clusters yield a set of interesting service/port behaviors, reflecting the aggregate behaviors of individual hosts on the corresponding ports. In the following, we introduce our definition of significance/interestingness using the (conditional) relative uncertainty measure (cf. Appendix A).

Given one feature dimension $X$ and a time interval $T$, let $m$ be the total number of flows observed during the time interval, and $A=\left{a_1, \ldots, a_n\right}, n \geq 2$, be the set of distinct values (e.g., srcIP’s) in $X$ that the observed flows take. Then the (induced) probability distribution $\mathcal{P}_A$ on $X$ is given by $p_i:=\mathcal{P}_A\left(a_i\right)=m_i / m$, where $m_i$ is the number of flows that take the value $a_i$ (e.g., having the $\left.\operatorname{srcIP} a_i\right)$. Then the (conditional) relative uncertainty, $R U\left(\mathcal{P}_A\right):=R U(X \mid A)$, measures the degree of uniformity in the observed features $A$. If $R U\left(\mathcal{P}_A\right)$ is close to 1 , say, $>\beta=0.9$, then the observed values are close to being uniformly distributed, and thus nearly indistinguishable. Otherwise, there are likely feature values in $A$ that “stand out” from the rest. We say a subset $S$ of $A$ contains the most significant (thus “interesting”) values of $A$ if $S$ is the smallest subset of $A$ such that (i) the probability of any value in $S$ is larger than those of the remaining values; and (ii) the (conditional) probability distribution on the set of the remaining values, $R:=A-S$, is close to being uniformly distributed, i.e., $R U\left(\mathcal{P}_R\right):=R U(X \mid R)>\beta$. Intuitively, $S$ contains the most significant feature values in $A$, while the remaining values are nearly indistinguishable from each other.

To see what $S$ contains, order the feature values of $A$ based on their probabilities: let $\hat{a}1, \hat{a}_2, \ldots, \hat{a}_n$ be such as $\mathcal{P}_A\left(\hat{a}_1\right) \geq \mathcal{P}_A\left(\hat{a}_2\right) \geq \ldots \mathcal{P}_A\left(\hat{a}_n\right)$. Then $S=$ $\left{\hat{a}_1, \hat{a}_2, \ldots, \hat{a}{k-1}\right}$, and $R=A-S=\left{\hat{a}k, \hat{a}{k+1}, \ldots, \hat{a}n\right}$, where $k$ is the smallest integer such that $R U\left(\mathcal{P}_R\right)>\beta$. Let $\alpha^=\hat{a}{k-1}$. Then $\alpha^$ is the largest “cut-off” threshold such that the (conditional) probability distribution on the set of remaining values $R$ is close to being uniformly distributed. To extract $S$ from $A$ (thereby, the clusters of flows associated with the significant feature values), we take advantage of the fact that in practice the probability distribution of the feature values $\mathcal{P}_A$ in general obeys a power-law: only a relatively few values (with respect to $n$ ) have significant larger probabilities, i.e., $|S|$ is relatively small, while the remaining feature values are close to being uniformly distributed. Hence, we can efficiently search for the optimal cut-off threshold $\alpha^*$.

# 网络分析代考

## 有限元方法代写

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。

## 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。

## 统计代写|网络分析代写Network Analysis代考|Background of Network Behavior Analysis

statistics-lab™ 为您的留学生涯保驾护航 在代写网络分析Network Analysis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。我们的专家在代写网络分析Network Analysis代写方面经验极为丰富，各种代写网络分析Network Analysis相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

## 统计代写|网络分析代写Network Analysis代考|Information Theory and Entropy

In the literature, network traffic analysis relies on volume-based and distributionbased approaches to study traffic features. The volume-based approach focuses on the simple counting on the observations on the traffic features, and often provides many valuable summaries on network traffic, e.g., how many networked systems does a smartphone communicate with or how many outgoing and incoming IP data packet counts and byte counts does a web server send and receive during a 5 -minute time window. However, the volume-based approach lacks the ability to shed light on the variations, distributions, or patterns inside the absolute volumes. For example, two web servers, receiving the equal amount of one million IP data packets during the same time period, might exhibit dramatically different behavioral patterns. One server might communicate with thousands of random web browsers across the Internet which collectively send one million IP data packets, while another server might be under distributed denial-of-service (DDoS) attacks from exactly one million unique source IP addresses each of which sends one single TCP SYN segment. Therefore, the networking research community has developed distribution-based approaches for effectively distinguishing such different patterns under the same traffic volumes.

To complement the volume-based approach on traffic feature analysis, several research studies $[5,11,31,45]$ have introduced the distribution-based approach to characterize the distributions of traffic features via probability and entropy concepts from probability theory and information theory. Information essentially quantifies “the amount of uncertainty” contained in data [81]. Consider a random variable $X$ that may take $N_X$ discrete values. Suppose we randomly sample or observe $X$ for $m$ times, which induces an empirical probability distribution on $X, p\left(x_i\right)=m_i / m, x_i \in X$, where $m_i$ is the frequency or number of times we observe $X$ taking the value $x_i$. The (empirical) entropy of $X$ is then defined as
$$H(X):=-\sum_{x_i \in X} p\left(x_i\right) \log p\left(x_i\right),$$
where by convention $0 \log 0=0$.
Entropy measures the “observational variety” in the observed values of $X$ [17]. Note that unobserved possibilities (due to $0 \log 0=0$ ) do not enter the measure, and $0 \leq H(X) \leq H_{\max }(X):=\log \min \left{N_X, m\right}$. $H_{\max }(X)$ is often referred to as the maximum entropy of (sampled) $X$, as $2^{H_{\max }(X)}$ is the maximum number of possible unique values (i.e., “maximum uncertainty”) that the observed $X$ can take in $m$ observations.

## 统计代写|网络分析代写Network Analysis代考|Standardized Entropy and Relative Uncertainty

Clearly, the entropy measure $H(X)$ is a function of the support size $N_X$ and sample size $m$. Assuming that $m \geq 2$ and $N_X \geq 2$ (otherwise there is no “observational variety” to speak of), we define the standardized entropy below-referred to as relative uncertainty (RU), as it provides an index of variety or uniformity regardless of the support or sample size:
$$R U(X):=\frac{H(X)}{H_{\max }(X)}=\frac{H(X)}{\log \min \left{N_X, m\right}}$$
Clearly, if $R U(X)=0$, then all observations of $X$ are of the same kind, i.e., $p(x)=1$ for some $x \in X$; thus observational variety is completely absent. More generally, let $A$ denote the (sub)set of observed values in $X$, i.e., $p\left(x_i\right)>0$ for $x_i \in A$. Suppose $m \leq N_X$. Then $R U(X)=1$ if and only if $|A|=m$ and $p\left(x_i\right)=1 / m$ for each $x_i \in A$. In other words, all observed values of $X$ are different or unique, thus the observations have the highest degree of variety or uncertainty. Hence, when $m \leq N_X$, $R U(X)$ provides a measure of “randomness” or “uniqueness” of the values that the observed $X$ may take-this is what is mostly used in network traffic analysis, as in general $m \ll N_X$.

In the case of $m>N_X, R U(X)=1$ if and only if $m_i=m / N_X$, thus $p\left(x_i\right)=$ $1 / N_X$ for $x_i \in A=X$, i.e., the observed values are uniformly distributed over $X$. In this case, $R U(X)$ measures the degree of uniformity in the observed values of $X$. As a general measure of uniformity in the observed values of $X$, we consider the conditional entropy $H(X \mid A)$ and conditional relative uncertainty $R U(X \mid A)$ by conditioning $X$ based on $A$. Then we have $H(X \mid A)=H(X), H_{\max }(X \mid A)=\log |A|$, and $R U(X \mid A)=H(X) / \log |A|$. Hence, $R U(X \mid A)=1$ if and only if $p\left(x_i\right)=1 /|A|$ for every $x_i \in A$. In general, $R U(X \mid A) \approx 1$ means that the observed values of $X$ are closer to being uniformly distributed, thus less distinguishable from each other, whereas $R U(X \mid A) \ll 1$ indicates that the distribution is more skewed, with a few values more frequently observed.

# 网络分析代考

## 统计代写|网络分析代写Network Analysis代考|Information Theory and Entropy

$X, p\left(x_i\right)=m_i / m, x_i \in X$ ，在哪里 $m_i$ 是我们观察的频率或次数 $X$ 取值 $x_i$. 的 (经验) 熵 $X$ 然后定义为
$$H(X):=-\sum_{x_i \in X} p\left(x_i\right) \log p\left(x_i\right)$$

## 统计代写|网络分析代写Network Analysis代考|Standardized Entropy and Relative Uncertainty

$$\left.\left.R \cup(X):=\mid f r a c{H(X)} H_{-}{\backslash \max }(X)\right}=\mid f r a c{H(X)} \backslash \backslash \log \backslash \min \backslash \text { left }\left{N_{-} X, \operatorname{m} \backslash r i g h t\right}\right}$$

## 有限元方法代写

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。

## 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。

## MATH4364 Network Analysis课程简介

Network Analysis has become a widely adopted method for studying the interactions between social agents, information and infrastructures. The strong demand for expertise in network analysis has been fueled by the widespread acknowledgement that everything is connected and the popularity of social networking services. This interdisciplinary course introduces students to fundamental theories, concepts, methods and applications of network analysis in a practical manner. Students learn and practice hands-on skills in collecting, analyzing and visualizing network data.

## PREREQUISITES

Understand fundamental concepts and theories from the fields of social network analysis and network science.
Apply this knowledge to solve real-world, network-centric problems.
Use basic and advanced analysis methods and tools to visualize and analyze network data.

## MATH4364 Network Analysis HELP（EXAM HELP， ONLINE TUTOR）

THEOREM (2.2): Let $\left{E_w:\right.$ weVW $}$ be a collection of equilibration operators associated with the the following conditions.
(1′) $E_w \mathscr{F}=\mathscr{F}$ for some $\mathscr{F} \in \mathscr{L}$ implies that $\mathscr{F}$ satisfies (1.13) for this fixed $w$.
(2′) $E_w$ is a continuous mapping from $\mathscr{Z}$ to $\mathscr{Z}$.
(3′) $C\left(\overline{E_{\mathrm{w}} \mathscr{F}}\right) \leqq C(\overline{\mathscr{F}})$ for all $\mathscr{F} \in \mathscr{F}$.
(4) $C\left(E_w \mathscr{F}\right)=C(\mathscr{F})$ for some $\mathscr{F} \in \mathscr{Z}$ implies that $E_w \mathscr{F}=\mathscr{F}$.
Then any equilibration operator associated with $\mathscr{T}$ and constructed by composition of the above collection $\left{E_w: w \in \mathscr{W}\right}$ satisfies conditions $1-4$ of Theorem (2.1).

Proof: Assumption 1 follows easily from $1^{\prime}$ and the structure of an equilibration operator associated with a pair of nodes. Assumption 2 is an obvious consequence of 2 ‘. Similarly 3 follows immediately from $3^{\prime}$. Finally 4 follows by a combination of $3^{\prime}$ and $4^{\prime}$.

The above theorem reduces the problem of checking conditions 1-4 of Theorem (2.1) to the much simpler problem of checking conditions 1 ‘-4’ of Theorem (2.2).

Sometimes an equilibration operator $E$ associated with a transportation network satisfies conditions 1 and 2 of Theorem (2.1) but it does not satisfy (or at least we cannot prove that it satisfies) conditions 3 and 4 . Then of course we do not know whether $E$ induces an algorithm for the solution of $P_1[\mathscr{F}]$. Nevertheless the sequence $\left{E^n \mathscr{F}^{(0)}\right}$ may lead to the solution of problem $P_1[\mathscr{F}]$ as shown by the following theorem, the proof of which is similar to the proof of Theorem (2.1).

THEOREM (2.3): Suppose that an equilibration operator $E$ satisfies conditions 1,2 of Theorem (2.1). Suppose further that for some choice of $\mathscr{F}^{(0)}$ the sequence $\left{\overline{E^n \mathscr{F}(0)}\right}$ converges as $n \rightarrow \infty$. Then $\left{\overline{E^N \mathscr{F}^{(0)}}\right}$ converges to the solution $\overline{\mathscr{S}}_1$, of the problem $P_1$.

REMARK (2.1): We have seen that an equilibration operator $E$ which induces an algorithm for the solution of problem $P_1$ enables us to calculate through $(2.2)$ the unique $\bar{F}_1$ associated with a problem $P_1[\mathscr{F}]$. Then we know that $R\left[\bar{F}_1\right]$ is the set of solutions of problem $P_1$. The calculation of an element of $R\left[\bar{F}_1\right]$, given $\bar{F}_1$, amounts to finding a solution to the system (1.1), (1.4), which might be accomplished by phase 1 of the Simplex method. This requires a rather tedious calculation. However, as shown in the proof of Theorem (2.1), some elements of $R\left[\overline{\mathscr{F}}_1\right]$ can be obtained directly from the algorithm as limits of the convergent subsequences of $\left{E^{n \prime} \mathscr{F}^{(0)}\right}$. In particular, if $R\left[\overline{\mathscr{F}}_1\right]$ consists of a unique element then
$$E^N \mathscr{F}^{(0)} \rightarrow \mathscr{F}_1, n \rightarrow \infty$$

## Textbooks

• An Introduction to Stochastic Modeling, Fourth Edition by Pinsky and Karlin (freely
available through the university library here)
• Essentials of Stochastic Processes, Third Edition by Durrett (freely available through
the university library here)
To reiterate, the textbooks are freely available through the university library. Note that
you must be connected to the university Wi-Fi or VPN to access the ebooks from the library
links. Furthermore, the library links take some time to populate, so do not be alarmed if
the webpage looks bare for a few seconds.

Statistics-lab™可以为您提供uh.edu MATH4364 Network Analysis网络分析课程的代写代考辅导服务！请认准Statistics-lab™. Statistics-lab™为您的留学生涯保驾护航。

## 统计代写|网络分析代写Network Analysis代考|Analysis of brain networks: tools and methodologies

statistics-lab™ 为您的留学生涯保驾护航 在代写网络分析Network Analysis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。我们的专家在代写网络分析Network Analysis代写方面经验极为丰富，各种代写网络分析Network Analysis相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

## 统计代写|网络分析代写Network Analysis代考|Topological characterization of connectome networks

Once a brain graph is built, there is the need to characterize the obtained graph by measuring its relevant topological properties [26]. Different metrics from statistics, physics and the area of complex networks are widely used in the study of human brain networks. The main measures are the measures of influence of segregation and of integration [39]. Measures of influence aim to quantify the relevance (or the role) of a node in a network. In connectomics, most used measures are classical centrality measures.
Degree distribution and hub analysis
Brain network analysis aims to detect pivotal regions and their connections and the flow of information among them that enable the functions of the brain. In graph theory pivotal regions are usually defined as hub nodes and connections as bridge edges. Many studies, lesions on human brain, have suggested that hub and bridges are related to vital neurocognitive functions, and on the spreads of disease in clinical brain disorders [24]. It has been evinced that the loss of hubs or bridges could reduce the effective information flow through the brain network. These properties have been described even in several other mammalian species. The identification of hubs and bridges resides in the definition of central nodes through centrality measures.

Literature contains many studies that identify hubs and bridges using centrality measures. Here, we briefly introduce the biological meaning of such measures, and the interested reader may find many details in [47].

One of the first topological measures that has been used is the degree centrality that is defined as the number of edges that are bound to a given node. Therefore a high number of edges suggest an important role of the node. Such measure represent a local measure of centrality (i.e., the centrality of a node with respect to its neighbors). The degree centrality (CD) is defined as the number of edges connected to a node; it is used to quantify the local centrality of each node. Degree centrality is often analyzed for all the nodes. The degrees of all nodes in the network comprise the degree distribution, which is an important marker of network development and tolerance to faults [24].

## 统计代写|网络分析代写Network Analysis代考|Community detection in brain graphs

Community detection algorithms are useful to study the organization of brain networks [2], since they provide information about the modular organization of the networks and the presence of network hierarchies. Many studies have been applied to structural and functional brain networks and, more recently, to multimodal networks (i.e., networks that integrate both structural and functional aspects).

Communities are related to the distribution of the degrees [17] that presents significant nonhomogeneities, giving rise to the presence of some vertices that have many edges and few connecting other vertices. Therefore, a community may be defined as a group of nodes that have many edges and few edges connecting them to the rest of the graph. The presence of communities may be seen as indicative of a modular organization in a network. For instance, in PPI networks, protein complexes are evidenced by small dense communities.

In brain graphs, many works presented different definitions of communities, based on slightly different definition of modularity. Briefly, many authors tried to adapt the modularity definition to better elucidate both functional and structural networks, and to take into account differences among individuals and the impact of ageing and neurological diseases $[6,40]$. Therefore there is no complete agreement on the existence of a modularity structure, since the conclusion of the existing works is strictly related to the input data.

As evidence in a recent work [6], it should be noted that network organization of the human brain shows significant variations among individuals, and it is dependent on age and disease status. Therefore the main challenge of community discovery methods for neurosciences is the development of a methodological framework for evidencing modular structures and for discriminating healthy/diseased status for the purposes of multi-scale analysis and biomarker generation.

A recent study on athletes’ brain networks [19] reveals a novel interpretation of core-periphery and rich club organization of brain graph. It reports an unification of modularity and core periphery structure. Authors presented a novel approach based on the weighted stochastic block model, demonstrating that functional brain networks show rich mesoscale organization beyond that sought by modularity maximization techniques. The organization is also affected by ageing and by neurological disease.

# 网络分析代考

## 有限元方法代写

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。

## 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。

## 统计代写|网络分析代写Network Analysis代考|Human brain connectome

statistics-lab™ 为您的留学生涯保驾护航 在代写网络分析Network Analysis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。我们的专家在代写网络分析Network Analysis代写方面经验极为丰富，各种代写网络分析Network Analysis相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

## 统计代写|网络分析代写Network Analysis代考|Human brain connectome

The human brain known to be one of the most complex interconnected systems consists of 80 to 100 billion nerve cells or neurons. Neurons are connected by synapses and an estimated 100 trillion connections between them. Neurons are organized into regions given by the connections. Connections may be analyzed from an anatomical point of view, i.e., the set of physical connections among neurons, and from a functional point of view, i.e., functional regions composed of groups of neurons that perform different functions. Variations of the set of connections are strictly linked to the insurgency and progression of neurological diseases as demonstrated by many works $[4,9,13,29]$. From a neuroscience point of view, it is always interesting to elucidate how the brain actually work with the help of such a large interconnections of nerve cells. Consequently, the modeling of the whole system of the brain elements and their relations has become an emerging theme in neuroscience. Initially, it has been assumed that a particular function of a brain is mapped to a particular region of the brain consisting of millions of neurons. Recent studies, however, discovered certain new facts that such functional regions do not work in isolation, rather, they interact with each other to perform mental activities. Main pillars of such research area are the availability of novel technologies that able to investigate such connections and to model such data using graph theory.

The development of novel technologies for imaging the brain has given the researchers the possibility to deeply study the anatomical and functional organization of the brain. This caused the development of a new research field, named connectomics, that focuses on the organization of the connectome, i.e., the whole set of associations (both physical and functional) of the constitutive elements of the brain $[7,10,28,42]$. With the availability of high-resolution neuroimaging technologies, such as fMRI and diffusion spectrum imaging (DSI), it is now possible to study brain activities in silico and noninvasive way. Modern neuroimaging techniques open up new avenues to study the brain and its activities through the light of how complex interplay happens between the various neural cells. Hence, it is obvious to view the mechanism of interactions as network of neural cells.

The set of connections among neural cells inside the brain is termed as connectome. Connectome is the comprehensive wiring architecture of neural connection of brain. The production and study of connectomes are referred to as connectomics. Study of connectome having an increasingly prominent role in bioinformatics and neurosciences in general. The key assumption of this field is the modeling of the brain as a complex (heterogeneous) network based on data extracted from neuroimaging. Consequently, the analysis of the representation of such a graph is giving valuable knowledge to the researchers. Graph theory models the human brain as a network of nodes linked by edges. The nodes represent brain regions, whereas the edges represent fibre connections in structural data and temporal correlations in functional data. A mapping of brain image to network can be viewed illustratively as shown in Fig. 8.1.

To construct a connectome network from the neuroimaging data, one needs to define first the region of interests (nodes) and association among the nodes (edges). However, the definition of nodes and edges to form a connectome network depends highly on the imaging inputs used. The networks may be either structural or functional, representing static or dynamic behavior of the brain cell activities, respectively.

## 统计代写|网络分析代写Network Analysis代考|From neuroimaging to brain graph

The process of building a network, describing the brain, starts with the acquiring images from the brain. Images are then processed for the construction of the network. Next, an adjacency matrix is created that highlights the connectivity between the regions.

Differently from other application fields, e.g., protein interaction networks, some challenges are still open. One of the main challenges is the absence of a commonly accepted definition of a node in a brain network [42]. Therefore there exist two main approaches: structural connectomics (which uses single neurons as nodes) and functional connectomics (uses functional regions as nodes). There exist different imaging techniques that may be used to generate graphs. Interested reader may find some more details in [28].

In the following, we present the noninvasive neuroimaging solutions to yield structural and functional connectivity.

The workflow, as depicted in Fig. 8.2, starts with magnetic resonance imaging (MRI). The set of scans acquired in a single session is then used to register the locations of the brain with respect to a set of known regions. Functional connectivity is established using a time series of acquisitions and the analysis of corresponding voxels. The correlation between the time series of different voxels or, using aggregated measures, brain regions can be detected and represented as a correlation matrix (value ranging from -1 to 1 ). Connections among regions are then derived from the correlation matrix that may be interpreted as a weighted or a binary network after thresholding all the values.

On the other hand, structural connectivity is established by acquiring diffusion tensor imaging (DTI) or DSI. The post-processing of these images with tracking algorithms (both deterministic and probabilistic) yield to the individuation of a set of streamlines between brain regions. Once again the streamlines may be interpreted as a connection matrix, in which each element represents the connection (or its probability) among two brain regions.

An essential step in the analysis of both images is the subdivision of the brain into regions, also referred to as a parcelation process. Formally the brain parcelation is the process of partitioning the brain into a set of regions that are homogeneous (from a biological point of view) and non-overlapping.

# 网络分析代考

## 有限元方法代写

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。

## 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。

## MATH4364 Network Analysis课程简介

Network Analysis has become a widely adopted method for studying the interactions between social agents, information and infrastructures. The strong demand for expertise in network analysis has been fueled by the widespread acknowledgement that everything is connected and the popularity of social networking services. This interdisciplinary course introduces students to fundamental theories, concepts, methods and applications of network analysis in a practical manner. Students learn and practice hands-on skills in collecting, analyzing and visualizing network data.

## PREREQUISITES

Understand fundamental concepts and theories from the fields of social network analysis and network science.
Apply this knowledge to solve real-world, network-centric problems.
Use basic and advanced analysis methods and tools to visualize and analyze network data.

## MATH4364 Network Analysis HELP（EXAM HELP， ONLINE TUTOR）

Theorem 2.3.2. The maximum of number of nodes in a binary of height $k$ is $2^{k+1}-1$.

Proof. A tree with height $k$ contains $k-1$ level (root at level 0 ). ByTheorem 2.3.1, the maximum number of nodes at level $L$ is $2^L$. Therefore, maximum number of nodes in a tree of height $k$ is the sum of the geometric series.
$$N=\sum_{L=0}^{k-1} 2^L=\frac{2^{k+1}-1}{2-1}=2^{k+1}-1$$

Theorem 2.3.3. If $N$ be the total number of nodes, then the height of the tree is at most $N-1$ and at least $\left\lceil\log _2 N\right\rceil$.

Proof. In case of extreme skewed tree each level contains one node. Therefore, the height of the tree become $N-1$ (root at 0 level).

However, in case of perfect binary tree of height $k$, the maximum number of nodes (Theorem 2.3.2) is
\begin{aligned} N & =2^{k+1}-1 \ k+1 & =\log _2 N+1 \end{aligned}
therefore, $k=\left\lceil\log _2 N\right\rceil$

## Textbooks

• An Introduction to Stochastic Modeling, Fourth Edition by Pinsky and Karlin (freely
available through the university library here)
• Essentials of Stochastic Processes, Third Edition by Durrett (freely available through
the university library here)
To reiterate, the textbooks are freely available through the university library. Note that
you must be connected to the university Wi-Fi or VPN to access the ebooks from the library
links. Furthermore, the library links take some time to populate, so do not be alarmed if
the webpage looks bare for a few seconds.

Statistics-lab™可以为您提供uh.edu MATH4364 Network Analysis网络分析课程的代写代考辅导服务！请认准Statistics-lab™. Statistics-lab™为您的留学生涯保驾护航。

## 统计代写|网络分析代写Network Analysis代考|Network visualization and analysis tools

statistics-lab™ 为您的留学生涯保驾护航 在代写网络分析Network Analysis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。我们的专家在代写网络分析Network Analysis代写方面经验极为丰富，各种代写网络分析Network Analysis相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

## 统计代写|网络分析代写Network Analysis代考|Network visualization and analysis tools

A number of in silico visualization and analysis tools are available to provide user-friendly environment for the system biologist. However, some of the tools are even equally applicable in other type of networks too. They are either online web-based or standalone desktop versions, which differ from one another in their way of generating and presenting the networks. Most of the effort is the outcome of various researches in the area of GRN inference methods or techniques. They are normally limited to any single inference method. Some of the tools also provide benchmarking and synthetic data-generation facilities. Other than inference of GRN, visualization of networks are also considered as an integral component of the tools. Below we present a comprehensive study on various tools and review their features in order to help the biologists select appropriate tools that may suit their own requirements.

GeneNetWeaver GeneNetWeaver (GNW) [61] is a tool developed in Java for in silico benchmarking and performance evaluation of network inference methods. Benchmarking involves generating gene network structures; generating simulated data from these networks using adequate dynamical models. In GNW, subnetwork extraction starts with the extraction of modules, which are groups of genes that are highly connected in a random network from a given global interaction network. This tool is able to perform a network motif analysis from a set of network predictions and their corresponding benchmark networks. The accuracy of network inference can be assessed using standard metrics, such as precisionrecall (PR) and receiver operating characteristic (ROC) curves.
Cytoscape Cytoscape [63] is a desktop complex network analysis and visualization tool in life sciences. Additional features are available as plugins. For example, Cytoscape can visualize molecular interaction networks, and integrate with gene expression profiles and other state data. Cytoscape has three versions: Cytoscape 2.x, Cytoscape 3.x, and Cytoscape.js. Cytoscape.js is a successor of CytoscapeWeb. This tool is most suitable for large-scale network analysis since it can handle thousands of nodes and edges and still run smoothly. Cytoscape supports directed, undirected, and weighted graphs and comes along with powerful visual styles, thereby allowing users to change the properties of nodes or edges. Plenty of elegant layout algorithms, including cyclic and springembedded layouts are available for visualization.

## 统计代写|网络分析代写Network Analysis代考|Proteins and interaction graph

Protein molecules are the workhorses of the cell, performing and controlling almost all activities in an organism. Although some proteins may work alone, proteins usually collaborate with others to achieve their intended tasks. When proteins work together, the influences and interactions among them can be shown in terms of a graph. The human cell produces potentially 100,000 different proteins, where a gene may produce more than one pro-tein. The interactions among these proteins are responsible for many physiological activities in the body. Organisms vary in the number of their proteins and the number of interactions. Proteins and protein interactions of a large number of organisms are being determined, and are recorded in databases. According to one study [94], humans have ten times more protein interactions than the fruit fly, and 20 times more than the single-celled yeast. A simple but useful approach to viewing such a complex biological system is to represent it as a network of the interplay among the different molecules.

Thus, complex systems, such as protein-protein interactions (PPI), are usually studied computationally from a graph-theoretic perspective. Studies suggest that PPI networks (PIN) are conserved through evolution [89]. Highly connected proteins within a network are vital molecules and have been found to be more essential for survival than proteins with lower connectivity [43]. As a result, the interactions between protein pairs and the overall composition of the network are important for the overall functioning of an organism. Understanding conserved substructures through a comparative analysis of these networks can provide insights into a variety of biochemical processes. The ultimate goal of network alignment is to transfer knowledge of protein function from one species to another. Since sequence similarity metrics, such as BLAST bit scores [3], are not conclusive evidence of similar function, the purpose of aligning two PPI networks is to supplement sequence similarity with topological information so as to identify orthologs as accurately as possible.

The physical interaction between a pair of protein molecules takes place because of biochemical activities within a cell. The synthesis of protein may also be regulated by another protein. When proteins work together, the influences and interactions among them can be shown in terms of a graph [10]. A graph showing interactions among proteins in a single species is called a protein-protein interaction network (or map). In a PPI graph, the proteins are nodes, and molecular interactions between them are edges.

# 网络分析代考

## 统计代写|网络分析代写Network Analysis代考|Network visualization and analysis tools

GeneNetWeaver GeneNetWeaver (GNW) [61] 是一种用 Java 开发的工具，用于网络推理方法的计算机基准测试和性能评估。基准测试涉及生成基因网络结构；使用适当的动态模型从这些网络生成模拟数据。在 GNW 中，子网提取从模块提取开始，这些模块是来自给定全局交互网络的随机网络中高度连接的基因组。该工具能够根据一组网络预测及其相应的基准网络执行网络主题分析。可以使用标准指标评估网络推理的准确性，例如精确召回率 (PR) 和接受者操作特征 (ROC) 曲线。
Cytoscape Cytoscape [63] 是生命科学中的桌面复杂网络分析和可视化工具。附加功能可作为插件使用。例如，Cytoscape 可以可视化分子相互作用网络，并与基因表达谱和其他状态数据集成。Cytoscape 有三个版本：Cytoscape 2.x、Cytoscape 3.x 和 Cytoscape.js。Cytoscape.js 是 CytoscapeWeb 的继承者。该工具最适合大规模网络分析，因为它可以处理数千个节点和边缘，并且仍然可以平稳运行。Cytoscape 支持有向图、无向图和加权图，并具有强大的视觉样式，从而允许用户更改节点或边的属性。大量优雅的布局算法，包括循环布局和弹簧嵌入布局可用于可视化。

## 有限元方法代写

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。

## 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。

## 统计代写|网络分析代写Network Analysis代考|Post inference network analysis

statistics-lab™ 为您的留学生涯保驾护航 在代写网络分析Network Analysis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。我们的专家在代写网络分析Network Analysis代写方面经验极为丰富，各种代写网络分析Network Analysis相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

## 统计代写|网络分析代写Network Analysis代考|Network module detection

A set of correlated and coexpressed genes, often referred as a functional module, play a synergistic role during any disease or any biological activity. Genes participating in a common module may cause clinically similar diseases and shares the common genetic origin of their associated disease phenotypes. Identifying such modules may be helpful in system-level understanding of biological and cellular processes or the pathophysiologic basis of associated diseases. Formally, we can define a network module as follows:

Definition 6.5.1 (Network module). Given a network $\mathcal{G}$, a network module $\mathcal{M}_i=\left{\mathcal{V}^{\prime}, \mathcal{E}^{\prime}\right}$ is a densely connected subgraph of $\mathcal{G}$ $\left(\mathcal{M}_i \subseteq \mathcal{G}\right)$, where interconnectivity of $\mathcal{V}^{\prime}$ with respect to $\mathcal{E}^{\prime} \subseteq \mathcal{E}$ is higher in comparison to the rest of $\mathcal{V}$, i.e., $\mathcal{V}-\mathcal{V}^{\prime}$.

The first step in this analysis is the building of (weighted or unweighted) graph starting from experimental data. Next, a network module or community detection method is applied. Community discovery algorithm may be categorized using different parameters [24], e.g., on the nature of discovered modules (overlapping or not), on their structure (densely connected subgraph, graphlet-based). Here, we do not propose any other classification, and we selected some state-of-the-art algorithms, and we categorized them into two broad classes: (i) algorithms developed specifically for gene expression analysis, and (ii) algorithm for network analysis that may be used for such networks.

WGCNA [37] is a popular method to detect modules from gene networks. It receives the coexpression network as input representing correlations, and it applies a soft thresholding to remove the possibility of non-relevant edges under the hypothesis that communities are made of relevant edges. After the thresholding, it employs a fuzzy approach to extract (possibly overlapping) modules without any hypothesis on the internal structure. The method proposed in [58], builds a correlation network first using an adhoc method, and then it employs a spectral clustering to mine the obtained network. Therefore, it receives as input raw gene expression data, and it can find clusters without imposing any constraint on the structure. As in the case of the previous method, the FUMET (fuzzy network module extraction technique) algorithm [42] proposes a novel method for the construction of coexpression network and a network module extraction technique based on fuzzy set theoretic approach. It can handle both positive and negative correlations among genes. Module miner [41] is similar to FUMET in the building of correlation network, and it employs a different module extraction approach.

## 统计代写|网络分析代写Network Analysis代考|Ranking key diseased genes using network

To study the causes of complex diseases, researchers focus on detecting subnetwork of functionally interrelated genes forming a functional module. However, not all the genes within a module play key roles in disease formation. Rather, a very few genes are the pivotal genes. The latter are called marker genes. They are responsible for disrupting the normal cellular functionalities, causing diseases. They are often identified as transcription factor (TF) genes. TF binds with the promoter region of target genes and lead to abnormal expression of the genes. Identifying such key genes responsible for the formation of disease networks may help in designing disease-specific drugs. A number of prioritization schemes have been proposed in different literature. Majority of them adopt centrality analysis of the disease subnetworks. It has been observed that the outcome of such biomarker ranking or prioritization scheme is sensitive towards the input network.
Detection of marker genes responsible for a genetic disease is a difficult task. Many researchers have dedicated their work in detecting such genes using various ranking techniques. Cluvian [43] identifies key genes that are possibly responsible for Alzheimer’s disease by analyzing modules derived from Alzheimer’s disease (AD) coexpression networks. The networks first extract AD submodules and rank them based on $\mathrm{AD}$ pathway enrichment scores. Top ranked modules are further analyzed topologically to identify central or hub genes, which are the potential key genes responsible for $\mathrm{AD}$. In $[39,48]$, they devised a ranking scheme using varied correlation measurements for the improvement of microarray and RNA-seq-based global and targeted coexpression networks. In addition to ordering genes based on fold change across the data, they also consider all three cell type-associated measures. In another attempt, authors considered a gene as a marker gene when genes are differentially expressed during some conditions or during protein interaction [74]. HyDRA (hybrid distance-score rank aggregation) [35], applies score-based and combinatorial aggregation techniques. It integrates a top-versus-bottom (TvB) weighting feature into the hybrid schemes. Using this scheme, it considers only top candidate genes. Biomarker ensemble ranking framework (BERF) [16] is developed for the detection of genes responsible for depression. This method employs two ranking models. It considers genes, which are already known marker and nonmarker genes. For a generation of ranking results, it uses an ensemble technique. HetRank [17] is a technique used for ranking gene on interaction network data. The algorithm focuses on two folds; the first fold concentrates on showing that genes triggering a disease are usually interconnected in PPI networks; whereas in the second fold, the method concentrates on genes expressing with varied pathogenic variations and their neighboring genes are marker genes.

# 网络分析代考

## 统计代写|网络分析代写Network Analysis代考|Network module detection

WGCNA [37] 是一种流行的检测基因网络模块的方法。它接收共表达网络作为表示相关性的输入，并在社区由相关边组成的假设下应用软阈值来消除不相关边的可能性。在阈值处理之后，它采用模糊方法来提取（可能重叠的）模块，而无需对内部结构进行任何假设。[58] 中提出的方法首先使用 adhoc 方法构建相关网络，然后使用谱聚类来挖掘获得的网络。因此，它接收原始基因表达数据作为输入，并且可以在不对结构施加任何约束的情况下找到聚类。与前面的方法一样，FUMET（模糊网络模块提取技术）算法[42]提出了一种构建共表达网络的新方法和基于模糊集理论方法的网络模块提取技术。它可以处理基因之间的正相关和负相关。Module miner [41]在相关网络的构建方面与FUMET相似，它采用了不同的模块提取方法。

## 有限元方法代写

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。

## 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。

## OLET2346 Network Analysis课程简介

Network Analysis has become a widely adopted method for studying the interactions between social agents, information and infrastructures. The strong demand for expertise in network analysis has been fueled by the widespread acknowledgement that everything is connected and the popularity of social networking services. This interdisciplinary course introduces students to fundamental theories, concepts, methods and applications of network analysis in a practical manner. Students learn and practice hands-on skills in collecting, analyzing and visualizing network data.

## PREREQUISITES

Understand fundamental concepts and theories from the fields of social network analysis and network science.
Apply this knowledge to solve real-world, network-centric problems.
Use basic and advanced analysis methods and tools to visualize and analyze network data.

## OLET2346 Network Analysis HELP（EXAM HELP， ONLINE TUTOR）

List five types of social networks based on different relationship types (e.g. friendship, follower, colleague) ? Explain each with an example? (20 points)

What is a Protein-Protein Interaction (PPI) network? Mention two applications of PPI. ( 20 points)

What is the centrality measure of a network? What are different types of centrality measure? Explain with an example. (20 points)

What is Multiplexity, Mutuality, and Assortativity in a network?

## Textbooks

• An Introduction to Stochastic Modeling, Fourth Edition by Pinsky and Karlin (freely
available through the university library here)
• Essentials of Stochastic Processes, Third Edition by Durrett (freely available through
the university library here)
To reiterate, the textbooks are freely available through the university library. Note that
you must be connected to the university Wi-Fi or VPN to access the ebooks from the library
links. Furthermore, the library links take some time to populate, so do not be alarmed if
the webpage looks bare for a few seconds.

Statistics-lab™可以为您提供sydney.edu OLET2346 Network Analysis网络分析课程的代写代考辅导服务！请认准Statistics-lab™. Statistics-lab™为您的留学生涯保驾护航。

## 统计代写|网络分析代写Network Analysis代考|CSE416a

statistics-lab™ 为您的留学生涯保驾护航 在代写网络分析Network Analysis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。我们的专家在代写网络分析Network Analysis代写方面经验极为丰富，各种代写网络分析Network Analysis相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

## 统计代写|网络分析代写Network Analysis代考|Rich club coefficient

The rich-club coefficient, introduced by Zhou and Mondragon in the context of the Internet topology [36], refers to the tendency of high-degree nodes (i.e., the hubs) in the network, to be very well-connected to other hub nodes. The name “rich-club” arises from the metaphor that the nodes with a large number of links, i.e., the hubs are “rich”, and they tend to be tightly and wellinterconnected between themselves, forming subgraphs called “club”. The rich-club coefficient is nothing but the measure of connectedness density within the club. A network with a rich club organization is shown in Fig. 4.4 for better understanding.

The nodes in a network can be categorized by a ranking scheme [36] or by their degree [8]. The rank $r$ of a node represents the corresponding position of the node in the list of descending order of node degrees, i.e., the most highly-connected node is ranked as $r=1$, the second best-connected node is $r=2$, and so on. The density of connections between the $r$ richest nodes is evaluated by the rich-club coefficient [36],
$$\Phi(r)=\frac{2 E(r)}{r(r-1)},$$
where $E(r)$ is the total number of links between $r$ hub nodes and $r(r-1) / 2$ is the maximum possible number of links among these nodes. Similarly, the rich-club coefficient [8] in terms of node de-gree can be represented as follows:
$$\Phi(k)=\frac{2 E_k}{N_k\left(N_k-1\right)},$$
where $E_k$ is the number of links present between the nodes of degree greater than or equal to $k$, and $N_k$ is the number of such nodes. Therefore, $\Phi(k)$ measures the fraction of actual links connecting those nodes and the maximum number of possible links. This measure explicitly reflects how densely connected are the nodes within a network.

The behavior of the rich-club coefficient is proportional to the value of $k$. It means, a rich-club coefficient increasing with the degree $k$ indicates that there exists a rich-club of nodes, which are densely interconnected than the nodes with smaller degrees. Contrarily, a decrease in the value of $\Phi(k)$ indicates the presence of many loosely connected and relatively independent subgroups. It is known as rich-club phenomenon.

## 统计代写|网络分析代写Network Analysis代考|Assortativity

Assortativity or assortative mixing was introduced by Newman [21]; is the tendency of nodes of a network (like social networks) to associate with others that are similar in some way. On the other hand, in nonsocial networks, such as biological networks, nodes with a high degree have a preference to associate with low-degree nodes. This tendency is referred as disassortative mixing, or disassortativity.

Assortativity is often quantified by the Pearson correlation between the excess degree distribution $q_k$ and the joint probability distribution $e_{j, k}[21]$. The excess degree is the number of edges leaving the node, other than the one that connects the pair. Similarly, the joint probability distribution is the distribution of the excess degrees of the two nodes at either end of a randomly chosen link. For an undirected graph, the assortativity is measured in terms of normalized Pearson coefficient of $e_{j, k}$ and $q_k$, and can be defined as
$$\rho=\frac{\sum_{j k} j k\left(e_{j k}-q_j q_k\right)}{\sigma_q^2}$$
where, $\delta$ is the standard deviation the of remaining degree distribution and $q_k$ is derived from the degree distribution $P_k$ as
$$q_k=\frac{(k+1) P_{k+1}}{\sum_{j \geq 1} j P_j}$$
In general, $\rho$ has a range from -1 to 1 , where 1 means a network has perfect assortativity, i.e., all nodes connect only with the nodes of a similar degree. If $\rho=0$, then the network has no assortativity, which means any node can randomly connect to any other node. Whereas, at $\rho=-1$, the network is completely disassortative; all nodes connect with the nodes of different degrees.

# 网络分析代考

## 统计代写|网络分析代写Network Analysis代考|Rich club coefficient

Zhou 和 Mondragon 在互联网拓扑的背景下引入的富倶乐部系数 [36]，指的是网络中高度节点（即中 心）与其他中心连接良好的趋势节点。“rich-club”这个名字来源于比喻具有大量链接的节点，即hubs是 “rich”，它们之间往往紧密且良好地相互联系，形成称为“club”的子图。富人倶乐部系数不过是衡量倶乐部 内部联系密度的指标。为了更好地理解，图 4.4 显示了具有丰富倶乐部组织的网络。

$$\Phi(r)=\frac{2 E(r)}{r(r-1)},$$

$$\Phi(k)=\frac{2 E_k}{N_k\left(N_k-1\right)},$$

## 统计代写|网络分析代写Network Analysis代考|Assortativity

Newman [21] 介绍了 Assortativity 或 assortative mixing；是网络节点（如社交网络) 与以某种方式相 似的其他节点关联的趋势。另一方面，在生物网络等非社交网络中，度数高的节点倾向于与度数低的节点 关联。这种趋势被称为异配混合或异配。

$$\rho=\frac{\sum_{j k} j k\left(e_{j k}-q_j q_k\right)}{\sigma_q^2}$$

$$q_k=\frac{(k+1) P_{k+1}}{\sum_{j \geq 1} j P_j}$$

## 有限元方法代写

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。

## 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。