Name
Abstract | ||
|---|---|---|
The evaluation of a user's social influence is essential for various applications in online social networks (OSNs). We propose a fine-grained feature-based social influence (FBI) evaluation model. First, we construct a user's initial social influence by exploring two essential factors, that is, the possibility of impacting others and the importance of the user himself. Second, we design the social influence adjustment model based on the PageRank algorithm by identifying the influence contributions of friends. For the aim of fine-grained evaluation, based on a feature set which includes the related topics and user profiles, we differentiate the feature strength of users and the tie strength of user relations. We also emphasize the effects of common neighbors in conducting influence between two users. Through experimental analysis, our FBI model shows remarkable performance, which can identify all users' social influences with much less duplication (it is less than 7 percent with our model, while more than 80 percent with other degree-based models), while having a larger influence spread with top- k influential users. A case study validates that our model can identify influential users with higher quality. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TPDS.2013.135 | IEEE Trans. Parallel Distrib. Syst. |
Keywords | Field | DocType |
fine-grained feature-based social influence evaluation,social influence,feature strength,osn,user social influences,pagerank algorithm,tie strength,online social networks,social influence adjustment model,social networking (online),common neighbors,fbi evaluation model,social sciences computing | World Wide Web,Social network,Tie strength,Information retrieval,Computer science,Pagerank algorithm,Feature set,Social influence,Feature based,Distributed computing | Journal |
Volume | Issue | ISSN |
25 | 9 | 1045-9219 |
Citations | PageRank | References |
23 | 0.79 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guojun Wang | 1 | 1740 | 144.41 |
| Wenjun Jiang | 2 | 356 | 24.25 |
| jie wu | 3 | 327 | 47.55 |
| Zhengli Xiong | 4 | 30 | 1.31 |