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加权复杂网络社团的评价指标及其发现算法分析
Analysis of community evaluation criterion and discovery algorithm of weighted complex network
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摘要: 节点的聚集现象是复杂网络的重要特性．以往研究主要发现无权复杂网络中的社团，较少涉及加权网络的社团发现．由于加权网络的复杂性远高于无权网络，一般认为加权网络的社团发现是一个较难的问题．本文基于统一的数据基础，从社团评价指标的有效性和现有算法的效果两个角度开展研究．首先，总结了加权网络三种常见的社团评估指标，并在社团大小、密度和局域特点均不同的模拟数据集上分析指标的有效性；其次，针对5个数据集，分析现有的3种加权复杂网络社团发现算法的效果．研究表明：上述指标无论在评价最基本的社团结构，还是在分析结构复杂的社团时都有较大缺欠；现有的加权网络社团发现算法的泛化能力不强．Abstract: The clustering of nodes is an important feature of complex network. Previous researches mainly focus on community discovery in unweighted network, with little attention paid to the weighted network because of the complexity of weighted network. The community discovery of the weighted network is believed to be a much more difficult task. In this paper, we perform a study on the effectivenesses of community evaluation criterion and the performances of the existing discovery algorithms. First, we summarize three classical community evaluation criterions of weighted network, and analyze their effecfivenesses according to a simulated noisy dataset, which has different community sizes, densities and local characteristics. Second, we adopt five datasets to compare the performances of three typical community discovery algorithms. The study shows that the existing criterions encounter difficulties in evaluating the basic community structure and in evaluating the weighted community with complex structure, and the generalization ability of the typical community discovery algorithm of weighted network is unsatisfactory.
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