Name
Abstract | ||
|---|---|---|
Nowadays, the analysis of social networks, as well as the community evolution has become a hotly discussed topic in social computing field. In this paper, we focus on mining and tracking the dynamic communities based on social networking analysis. Based on a generic framework for the dynamic community discovery, a computational approach is developed to extract users' static and dynamic features for the temporal trend detection. A dynamically socialized user networking model is then presented to describe users' various social relationships. A mechanism is proposed and developed to detect the dynamic user communities, and track their evolving changes. Experiments using Twitter data demonstrate the effectiveness of our method in tracking how communities dynamically create, split, and merge from a group of connected people in social media environments. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CIT.2016.74 | 2016 IEEE International Conference on Computer and Information Technology (CIT) |
Keywords | Field | DocType |
social network analysis,user correlation,community mining,dynamics tracking | Data science,Social network,Community mining,Social media,Computer science,Social network analysis,Feature extraction,Socialization,Social computing,Market research | Conference |
ISBN | Citations | PageRank |
978-1-5090-4315-6 | 0 | 0.34 |
References | Authors | |
18 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaokang Zhou | 1 | 225 | 25.50 |
| Wei Liang | 2 | 67 | 6.75 |
| Bo Wu | 3 | 21 | 4.63 |
| Zixian Lu | 4 | 4 | 1.43 |
| Shoji Nishimura | 5 | 3 | 2.79 |
| Takashi Shinomiya | 6 | 6 | 3.38 |
| Jin, Q. | 7 | 233 | 33.40 |