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Community Detection in Multi-relational Social Networks

机译：多关系社交网络中的社区检测

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摘要

Multi-relational networks are ubiquitous in many fields such as bibliography, twitter, and healthcare. There have been many studies in the literature targeting at discovering communities from social networks. However, most of them have focused on single-relational networks. A hint of methods detected communities from multi-relational networks by converting them to single-relational networks first. Nevertheless, they commonly assumed different relations were independent from each other, which is obviously unreal to real-life cases. In this paper, we attempt to address this challenge by introducing a novel co-ranking framework, named MutuRank. It makes full use of the mutual influence between relations and actors to transform the multi-relational network to the single-relational network. We then present GMM-NK (Gaussian Mixture Model with Neighbor Knowledge) based on local consistency principle to enhance the performance of spectral clustering process in discovering overlapping communities. Experimental results on both synthetic and real-world data demonstrate the effectiveness of the proposed method.
机译：多关系网络在许多领域普遍存在，如参考书目，推特和医疗保健。在从社交网络发现社区的文献中有很多研究。然而，他们中的大多数都专注于单一关系网络。通过将它们转换为首先将它们转换为单个关系网络来检测来自多关系网络的社区的一丝提示。然而，他们通常认为不同的关系彼此独立，这显然是与现实生活案例的不真实。在本文中，我们试图通过引入一个名为Muturank的新型共同排名框架来解决这一挑战。它充分利用了关系与演员之间的相互影响力将多关系网络转换为单一关系网络。然后，我们基于局部一致性原理呈现GMM-NK（具有邻居知识的高斯混合模型，以增强光谱聚类过程在发现重叠社区中的性能。合成和实世界数据的实验结果证明了该方法的有效性。

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