Mining directed social network from message board
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Statistical analysis of the social network and discussion threads in slashdot
Proceedings of the 17th international conference on World Wide Web
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Text classification using graph mining-based feature extraction
Knowledge-Based Systems
Hy-SN: Hyper-graph based semantic network
Knowledge-Based Systems
Topic oriented community detection through social objects and link analysis in social networks
Knowledge-Based Systems
Knowledge evolution course discovery in a professional virtual community
Knowledge-Based Systems
Topological analysis of knowledge maps
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
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As information technology has advanced, people are turning more frequently to electronic media for communication, and social relationships are increasingly found in online channels. Massive amounts of the real data collected from online social networks (e.g., Internet newsgroups, BBS, and chat rooms) are network structured. Discovering the latent communities therein is a useful way to better understand the properties of a virtual social network. However, community-detection tasks were infeasible in previous studies of online social networks, especially with large-scale or weighted networks. In this paper, we constructed a semantic network using the semantic information extracted from comment content. In our modeling, we considered the impact of the weight on every edge and focused on the ''giant component'' of the online social network to reduce computational complexity; thus, our method can handle large-scale networks. In the experimental work, we evaluated our method using real datasets and compared our approach with several previous methods based on comment interactions; the results show that our method is much faster, more effective and robust.