Abstract
A new fast community unfolding and interests mining algorithm is proposed to solve the problem that traditional methods cannot effectively extract communities from large-scale directed networks. A greedy algorithm is used to maximize modularity so that the tradeoff between the accuracy and efficiency in the community mining of directed networks is better balanced and its application to large scale microblog networks can be realized. The users' interests in the extracted community are then further mined using the tf-idf algorithm to score the most-occurred phrases in the community. Experimental results based on Sina Microblog show that the proposed algorithm can not only find out the community structures and their interests quickly, but also can uncover the zombie-fans community efficiently and accurately. These results exhibit great values for system purification, rumors control and accurate delivery of online advertising in microblog systems.
| Original language | English |
|---|---|
| Pages (from-to) | 7-12 |
| Number of pages | 6 |
| Journal | Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University |
| Volume | 48 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 2014 |
Keywords
- Community mining
- Directed graph
- Microblog
- User interest groups
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