Detecting communities in social networks using label propagation with information entropy
Detecting communities in social networks using label propagation with information entropy
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DOI：
10.1016/j.physa.2016.12.047
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发表时间：
2017-04-01
影响因子：
3.3
通讯作者：
Cheng, Junjun
中科院分区：
文献类型：
--
作者：
Chen, Naiyue;Liu, Yun;Cheng, Junjun
社区发现已经成为理解真实的世界网络结构和功能的一种重要而有效的方法。标签传播算法（LPA）是一种用于检测非重叠社区的近线性时间算法。然而，它仅考虑直接相邻关系。在本文中，我们提出了一种算法，考虑信息熵作为直接邻居和间接邻居之间的关系的测量。在标签更新中，我们提出了一个新的归属系数来描述标签的权重。在归属系数不小于阈值的情况下，每个节点可以保留一个或多个标签，构成一个重叠社区。在真实网络和基准网络上的实验结果表明，该算法在检测网络社区结构方面也具有较高的准确率。(C)2016爱思唯尔B.V.保留所有权利。
Community detection has become an important and effective methodology to understand the structure and function of real world networks. The label propagation algorithm (LPA) is a near-linear time algorithm used to detect non-overlapping community. However, it merely considers the direct neighbor relationship. In this paper, we propose an algorithm to consider information entropy as the measurement of the relationship between direct neighbors and indirect neighbors. In a label update, we proposed a new belonging coefficient to describe the weight of the label. With the belonging coefficient no less than a threshold each node can keep one or more labels to constitute an overlapping community. Experimental results on both real-world and benchmark networks show that our algorithm also possesses high accuracy on detecting community structure in networks. (C) 2016 Elsevier B.V. All rights reserved.