### 统计代写|复杂网络代写complex networks代考| Algorithms for Community Detection

statistics-lab™ 为您的留学生涯保驾护航 在代写复杂网络complex networks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 networks方面经验极为丰富，各种代写复杂网络complex networks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|复杂网络代写complex networks代考|Comparing a Quality Function

Instead of comparing the output of an algorithm for networks with known community structure one may compare the results of different algorithms across a quality function for the assignment of nodes into communities. Newman and Girvan [23] have proposed the following measure of the “modularity” of a community structure with $q$ groups:
$$Q=\sum_{s=1}^{q} e_{s s}-a_{s t}^{2}, \text { with } a_{s}=\sum_{s=1}^{q} e_{\mathrm{T} s} .$$
Here, $e_{r s}$ is the fraction of all edges that connect nodes in groups $r$ and $s$ and hence $e_{s s}$ is the fraction of edges connecting the nodes of group $s$ internally. From this, one finds that $a_{s}$ represents the fraction of all edges having at least one end in group $s$ and $a_{s}^{2}$ is to be interpreted as the expected fraction of links falling between nodes of group $s$ given a random distribution of links. Note the similarity of this measure with the assortativity coefficient defined earlier. It is clear that $-1<Q<1$.

This modularity measure will play a central role in the following chapters and it is of course a natural idea to optimize the assignment of nodes in communities directly by maximizing the modularity of the resulting partition.

## 统计代写|复杂网络代写complex networks代考|Hierarchical Algorithms

A large number of heuristic algorithmic approaches to community detection have been proposed by computer scientists. The developments follow generally along the lines of the algorithms developed for multivariate data [24-26]. Typically, the problem is approached by a recursive min-cut technique that partitions a connected graph into two parts minimizing the number of edges to cut $[27,28]$. These treatments, however, suffer greatly from being very skewed as the min-cut is usually found by cutting off only a very small subgraph [29]. A number of penalty functions have been suggested to overcome this problem and balance the size of subgraphs resulting from a cut. Among these are ratio cuts $[29,30]$, normalized cuts [31] or min-max cuts [32].

The clustering algorithm devised by Girvan and Newman (GN) [17] was the first to introduce the problem of community detection to physics researchers in the field of complex networks. As is often the case, the impact the paper created was not merely for the algorithm but because of the well-chosen illustrative example of its application. GN’s algorithm is based on “edge betweenness” – a concept again borrowed from sociology. Given all geodesic paths between all pairs of nodes in the network, the betweenness of an edge is the number of such paths that run across it. It is intuitive that betweenness is a measure of centrality and hence introduces a measure of distance to the graph. The GN algorithm calculates the edge betweenness for all edges in the graph and then removes the edge with the highest betweenness. Then, the betweenness values for all edges are recalculated. This process is repeated until the network is split into two disconnected components and the procedure starts over again on each of the two components until only single nodes remain. The algorithm falls into the class of recursive partitioning algorithms and its output is generally depicted as a dendrogram illustrating the progression of splitting the network.

Figure $2.5$ illustrates the algorithm with the example chosen by GN [17]. The network shown displays the friendships among the members of a karate club at a US university compiled by the anthropologist Zachary [18] over a period of 2 years. Over the course of the observation an internal dispute between the manager (node 34) and the instructor of the club (node 1) led to the split up of the club. Roughly half of the members joined the instructor in the formation of a new club and the other half of the members stayed with the manager hiring a new instructor. It turns out that the first split induced by the GN algorithm corresponds almost exactly to the observed split among the members of the club. This led to the conclusion that the split could be “predicted” from the topology of the network and that the GN algorithm is able to make such predictions. As far as the definition of community is concerned, the algorithm induces a hierarchy of communities as at any level of progress of the algorithm a set of connected nodes is to be understood as a community.

## 统计代写|复杂网络代写complex networks代考|Semi-hierarchical

The hierarchical methods cited so far assume a nested hierarchy of communities. One of the few methods which allow for overlapping communities is the clique percolation method of Palla et al. $[8,22]$ which was introduced already. Even though the method allows a node to be part of more than one community, communities resulting from $k+1$-clique percolation processes are always contained within $k$-clique communities. It is never possible that the nodes contained in the overlap of two communities form their own community. Another problem of this method is its dependence on the existence of triangles in the network. Nodes which are not connected via triangles to communities can never be part of such communities and only nodes with at least $k-1$ links can be part of a k-clique at all. Also, this method may be easily mislead by the addition or removal of single links in the network, as a single link may be responsible for the joining of two communities into one. Clearly, this situation is unsatisfactory in case of noisy data.

## 广义线性模型代考

statistics-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。