Influence based Analysis of Community Consistency in Dynamic Networks

被引:0
|
作者
Jia, Xiaowei [1 ]
Li, Xiaoyi [2 ]
Du, Nan [2 ]
Zhang, Yuan [3 ]
Gopalakrishnan, Vishrawas [2 ]
Xun, Guangxu [2 ]
Zhang, Aidong [2 ]
机构
[1] Univ Minnesota Twin Cities, Minneapolis, MN 55455 USA
[2] SUNY Buffalo, Buffalo, NY USA
[3] North Carolina State Univ, Raleigh, NC 27695 USA
来源
PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING ASONAM 2016 | 2016年
基金
美国国家科学基金会;
关键词
STATE;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The development of Internet and social networks has provided more emerging network data which facilitates the dynamic network analysis. In this paper, we propose a new method to measure coherence strength, also referred to as community consistency, of a community under dynamic settings. In order to better interpret the influence of evolving community structure on community consistency, we model the problem as one of influence propagation processes having a causal relation with the community consistency. To this effect a generative model is proposed to combine the influence propagation and the network topological structure at each time stamp. Our comprehensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed framework in estimating the community consistency.
引用
收藏
页码:1 / 8
页数:8
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