User community discovery from multi-relational networks

被引:55
作者
Zhang, Zhongfeng [1 ]
Li, Qiudan [1 ]
Zeng, Daniel [1 ,2 ]
Gao, Heng [1 ]
机构
[1] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
[2] Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA
关键词
Community discovery; Multi-relational network; Author topic model; Non-negative matrix factorization; SOCIAL NETWORK;
D O I
10.1016/j.dss.2012.09.012
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
Online social network services (SNS) have been experiencing rapid growth in recent years. SNS enable users to identify other users with common interests, exchange their opinions, and establish forums for communication, and so on. Discovering densely connected user communities from social networks has become one of the major challenges, to help understand the structural properties of SNS and improve user-oriented services such as identification of influential users and automated recommendations. Previous work on community discovery has treated user friendship networks and user-generated contents separately. We hypothesize that these two types of information can be fruitfully integrated and propose a unified framework for user community discovery in online social networks. This framework combines the author-topic (AT) model with user friendship network analysis. We empirically show that this approach is capable of discovering interesting user communities using two real-world datasets. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:870 / 879
页数:10
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