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| Content Provider | ACM Digital Library |
|---|---|
| Author | Yu, Philip S. Wang, Guan Hu, Qingbo |
| Abstract | In the social network research, the studies on social influence maximization and entity similarity are two important and orthogonal tasks. On homogeneous networks, social influence maximization research tries to identify an initial influential set that maximizes the spread of the information, while similarity studies focus on designing meaningful ways to quantify entities' similarities. When heterogeneous networks are becoming ubiquitous and entities of different types are related to each other, we observe the possibility of merging the two directions together to improve the performance for both of them. In fact, we found that influence values among one type of nodes and similarity scores among the other type of nodes reinforce each other towards better and more meaningful results. Therefore, we introduce a framework that computes social influence for one type of nodes and simultaneously measures similarity of the other type of nodes in a heterogeneous network. First, we decouple the target heterogeneous network (or we call it Influence Similarity (IS) network) into three different parts: Influence network, Similarity network and information tunnels (IT) between them. Through IT, we exchange the influence scores and the similarity scores to calculate more precise similarity and influence scores in order to improve both of their qualities. The experiment results on real world data shows that our framework enables influence maximization framework to identify more influential seeds in Influence network and similarity measures to produce more meaningful similarity scores in Similarity network simultaneously. |
| Starting Page | 1462 |
| Ending Page | 1466 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781450311564 |
| DOI | 10.1145/2396761.2398453 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-10-29 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Similarity Influence Social networks |
| Content Type | Text |
| Resource Type | Article |
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