Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
The Journal of Machine Learning Research
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The political blogosphere and the 2004 U.S. election: divided they blog
Proceedings of the 3rd international workshop on Link discovery
Efficient aggregation for graph summarization
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A framework for WWW user activity analysis based on user interest
Knowledge-Based Systems
Topic and role discovery in social networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Improved trust-aware recommender system using small-worldness of trust networks
Knowledge-Based Systems
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
Knowledge sharing in dynamic virtual enterprises: A socio-technological perspective
Knowledge-Based Systems
Community detection based on a semantic network
Knowledge-Based Systems
An O(n2) algorithm for detecting communities of unbalanced sizes in large scale social networks
Knowledge-Based Systems
A sock puppet detection algorithm on virtual spaces
Knowledge-Based Systems
Community Detection in Complex Networks: Multi-objective Enhanced Firefly Algorithm
Knowledge-Based Systems
Using coalitional games to detect communities in social networks
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
A game theory based approach for community detection in social networks
BNCOD'13 Proceedings of the 29th British National conference on Big Data
| Hi-index | 0.00 |
Community detection is an important issue in social network analysis. Most existing methods detect communities through analyzing the linkage of the network. The drawback is that each community identified by those methods can only reflect the strength of connections, but it cannot reflect the semantics such as the interesting topics shared by people. To address this problem, we propose a topic oriented community detection approach which combines both social objects clustering and link analysis. We first use a subspace clustering algorithm to group all the social objects into topics. Then we divide the members that are involved in those social objects into topical clusters, each corresponding to a distinct topic. In order to differentiate the strength of connections, we perform a link analysis on each topical cluster to detect the topical communities. Experiments on real data sets have shown that our approach was able to identify more meaningful communities. The quantitative evaluation indicated that our approach can achieve a better performance when the topics are at least as important as the links to the analysis.