本文探讨了三种近线性社区检测算法在真实世界网络中与实际社区结构的关系。研究发现，在 Orkut 和 DBPedia 两个网络上，这些算法未能提供良好的社区结构，并将大部分节点分配给了少数几个大型集群。进一步的研究引入了度 assortativity 系数来衡量节点倾向于与其他相似度节点连接的趋势。实验表明，在调整连接 disassortative 节点的边权重后，社区检测方法的效果得到了显著改善。
Paper title: On Community Detection in Real-world Networks and the Importance of Degree Assortativit
Author: Marek Ciglan, Michal Laclavik, Kjetil Norvag
Conference: KDD 2013, Chicago, Illinois, USA
Year: 2013
This paper studies the relation between thedetected communities of three near-linear community detection algorithms andthe ground truth community. The results show that on two networks (Orkut andDBPedia) these community detection methods fail to deliver good communitystructure. A further investigationreveals that the majority of the nodes have been signed to a few of the largestclusters on the two networks.
Based on the above observation, the author studies a measure named degree assortativity coefficient, which denotes a tendency of nodes to be connected with other nodes of similar degree. The results show that networks with assortative community structure have important parts of communities composed of edges connecting nodes with similar degrees. Then the author proposes to weight the edges in a way that lower the weight of the edges connecting disassortative nodes. Two edge-weighting functions are presented to lower the influence of the edges connecting low and high degree nodes and the experiments prove that the two functions can significantly improve the results of community detection methods on networks with assotative community structure.
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