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Finding overlapping communities based on information fusion in social network

机译：在社交网络中基于信息融合寻找重叠社区

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摘要

Social network is becoming indispensable of people's lives in recent years. Community detection on real network continues to be a hotspot in data ming domain. As users may join multiple social circles and interest communities, and an abundance of information can be a reflection of users' preference, heterogeneous information fusion and overlapping community detection are two key issues researchers have to face. This paper presents an overlapping community detection model IF-COPRA that incorporated heterogeneous information into an integrated user adjacency diagram, based on which multi-label propagation and overlapping community detection are fulfilled. Before label propagation, user adjacency diagram is pruned to eliminate noisy relationship and vertices are processed in degree otder to enlarge the influence of high degree vertices and improve the robustness of IF-COPRA. Experiments on real world data sets demonstrate that IF-COPRA model performances better than baseline algorithms in most cases.
机译：近年来，社交网络已成为人们生活中不可缺少的一部分。真实网络上的社区检测仍然是数据挖掘领域的热点。由于用户可能会加入多个社交圈和感兴趣的社区，并且大量的信息可以反映用户的偏好，因此异构信息融合和社区检测重叠是研究人员必须面对的两个关键问题。本文提出了一种重叠社区检测模型IF-COPRA，该模型将异构信息整合到一个集成的用户邻接图中，在此基础上实现了多标签传播和重叠社区检测。在标签传播之前，修剪用户邻接图以消除噪声关系，并以度为单位处理顶点，以扩大高度顶点的影响并提高IF-COPRA的鲁棒性。对现实世界数据集的实验表明，在大多数情况下，IF-COPRA模型的性能要优于基线算法。

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