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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2016 16th IEEE International Conference on Computer and Information Technology, CIT 2016, 2016 6th International Symposium on Cloud and Service Computing, IEEE SC2 2016 and 2016 International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 177-182 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509043149 |
| DOIs | |
| Publication status | Published - 2017 Mar 10 |
| Event | 16th IEEE International Conference on Computer and Information Technology, CIT 2016 - Nadi, Fiji Duration: 2016 Dec 7 → 2016 Dec 10 |
Other
| Other | 16th IEEE International Conference on Computer and Information Technology, CIT 2016 |
|---|---|
| Country | Fiji |
| City | Nadi |
| Period | 16/12/7 → 16/12/10 |
Fingerprint
Keywords
- Community mining
- Dynamics tracking
- Social network analysis
- User correlation
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Computer Networks and Communications
- Information Systems
- Safety, Risk, Reliability and Quality
Cite this
Dynamic community mining and tracking based on temporal social network analysis. / Zhou, Xiaokang; Liang, Wei; Wu, Bo; Lu, Zixian; Nishimura, Shoji; Shinomiya, Takashi; Jin, Qun.
Proceedings - 2016 16th IEEE International Conference on Computer and Information Technology, CIT 2016, 2016 6th International Symposium on Cloud and Service Computing, IEEE SC2 2016 and 2016 International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 177-182 7876335.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Dynamic community mining and tracking based on temporal social network analysis
AU - Zhou, Xiaokang
AU - Liang, Wei
AU - Wu, Bo
AU - Lu, Zixian
AU - Nishimura, Shoji
AU - Shinomiya, Takashi
AU - Jin, Qun
PY - 2017/3/10
Y1 - 2017/3/10
N2 - 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.
AB - 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.
KW - Community mining
KW - Dynamics tracking
KW - Social network analysis
KW - User correlation
UR - http://www.scopus.com/inward/record.url?scp=85017335898&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85017335898&partnerID=8YFLogxK
U2 - 10.1109/CIT.2016.74
DO - 10.1109/CIT.2016.74
M3 - Conference contribution
AN - SCOPUS:85017335898
SP - 177
EP - 182
BT - Proceedings - 2016 16th IEEE International Conference on Computer and Information Technology, CIT 2016, 2016 6th International Symposium on Cloud and Service Computing, IEEE SC2 2016 and 2016 International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -