Paper Info
Title
A Community Finding Method For Weighted Dynamic Online Social Network Based On User Behavior
Abstract
Revealing the structural features of social networks is vitally important to both scientific research and practice, and the explosive growth of online social networks in recent years has brought us dramatic advances to understand social structures. Here we proposed a community detection approach based on user interaction behavior in weighted dynamic online social networks. We researched interaction behaviors in online social networks and built a directed and unweighted network model in terms of the Weibo following relationships between social individuals at the very beginning. In order to refine the interaction behavior, level one fuzzy comprehensive evaluation model was employed to describe how closely individuals are connected to each other. According to this intimate degree description, weights are tagged to the prior unweighted model we built. Secondly, a heuristic community detection algorithm for dynamic network was provided based on the improved version of modularity called module density. As for the heuristic rule, we chose greedy strategy and merely fed the algorithms with the changed parts within neighboring time slice. Experimental results show that the proposed algorithm can obtain high accuracy and simultaneously get comparatively lower time complexity than some typical algorithms. More importantly, our algorithm needs no a priori conditions.
Year
DOI
Venue
2015
10.1155/2015/306160
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
Field
DocType
Volume
Dynamic network analysis,Heuristic,Social network,Computer science,Fuzzy logic,Artificial intelligence,Social structure,Time complexity,Machine learning,Network model,Modularity
Journal
11
ISSN
Citations 
PageRank 
1550-1477
0
0.34
References 
Authors
9
5
Name
Order
Citations
PageRank
Dongming Chen174.10
yanlin dong200.34
Xinyu Huang311315.42
haiyan chen451.13
Dongqi Wang5124.77