标题:Edge Representation Learning for Community Detection in Large Scale Information Networks
作者:Li, Suxue; Zhang, Haixia; Wu, Dalei; Zhang, Chuanting; Yuan, Dongfeng
通讯作者:Yuan, Dongfeng;Yuan, DF
作者机构:[Li, Suxue; Zhang, Haixia; Zhang, Chuanting; Yuan, Dongfeng] Shandong Univ, Shandong Prov Key Lab Wireless Commun Technol, Jinan 250100, Shandong, Peo 更多
会议名称:1st International Workshop on Mobility Analytics for Spatiotemporal and Social Data (MATES)
会议日期:SEP 01, 2017
来源:MOBILITY ANALYTICS FOR SPATIO-TEMPORAL AND SOCIAL DATA, MATES 2017
出版年:2018
卷:10731
页码:54-72
DOI:10.1007/978-3-319-73521-4_4
关键词:Network; Community detection; Representation learning; Cluster
摘要:It is found that networks in real world divide naturally into communities or modules. Many community detection algorithms have been developed to uncover the community structure in networks. However, most of them focus on non-overlapping communities and the applicability of these work is limited when it comes to real world networks, which inherently are overlapping in most cases, e.g. Facebook and Weibo. In this paper, we propose an overlapping community detection algorithm based on edge representation learning. Firstly, we sample a series of edge sequences using random walks on graph, then a mapping function from edge to feature vectors is automatically learned in an unsupervised way. At last we employ the traditional clustering algorithms, e.g. K-means and its variants, on the learned representations to carry out community detection. To demonstrate the effectiveness of our proposed method, extensive experiments are conducted on a group of synthetic networks and two real world networks with ground truth. Experiment results show that our proposed method outperforms traditional algorithms in terms of evaluation metrics.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041100088&doi=10.1007%2f978-3-319-73521-4_4&partnerID=40&md5=79c40e62597728ed455fd79a56152863
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