Name
Abstract | ||
|---|---|---|
Information diffusion, which addresses the issue of how a piece of information spreads and reaches individuals in or between networks, has attracted considerable research attention due to its widespread applications, such as viral marketing and rumor control. However, the process of information diffusion is complex and its underlying mechanism remains unclear. An important reason is that social influence takes many forms and each form may be determined by various factors. One of the major challenges is how to capture all the crucial factors of a social network such as users' interests (which can be represented as topics), users' attributes (which can be summarized as roles), and users' reposting behaviors in a unified manner to model the information diffusion process. To address the problem, we propose the joint information diffusion model (TRM) that integrates user topical interest extraction, role recognition, and information diffusion modeling into a unified framework. TRM seamlessly unifies the user topic role extraction, role recognition, and modeling of information diffusion, and then translates the calculations of individual level influence to the role-topic pairwise influence, which can provide a coarse-grained diffusion representation. Extensive experiments on two real-world datasets validate the effectiveness of our approach under various evaluation indices, which performs superior than the state-of-the-art models by a large margin. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-93037-4_1 | ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2018, PT II |
Keywords | Field | DocType |
User topic,User role,Information diffusion,Social network | Pairwise comparison,Viral marketing,Social network,Role model,Computer science,Rumor,Social influence,Artificial intelligence,Probabilistic logic,Diffusion (business),Machine learning | Conference |
Volume | ISSN | Citations |
10938 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hengpeng Xu | 1 | 3 | 1.09 |
| Jinmao Wei | 2 | 23 | 6.46 |
| Zhenglu Yang | 3 | 257 | 35.45 |
| Jianhua Ruan | 4 | 341 | 28.43 |
| Jun Wang | 5 | 1 | 1.02 |