Abstract
Detecting social communities in large social networks provides an effective way to analyze the social media users' behaviors and activities. It has drawn extensive attention from both academia and industry. One essential aspect of communities in social networks is outer influence which is the capability to spread internal information of communities to external users. Detecting the communities of high outer influence has particular interest in a wide range of applications, e.g., Ads trending analytics, social opinion mining and news propagation pattern discovery. However, the existing detection techniques largely ignore the outer influence of the communities. To fill the gap, this work investigates the Most Influential Community Search problem to disclose the communities with the highest outer influences. We firstly propose a new community model, maximal kr-Clique community, which has desirable properties, i.e., society, cohesiveness, connectivity, and maximum. Then, we design a novel tree-based index structure, denoted as C-Tree, to maintain the offline computed r-cliques. To efficiently search the most influential communities, we also develop four advanced index-based algorithms which improve the search performance of non-indexed solution by about 200 times. The efficiency and effectiveness of our solution have been extensively verified using six real datasets and a small case study.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2017 IEEE 33rd International Conference on Data Engineering (ICDE) |
| Place of Publication | San Diego, California |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 871-882 |
| Number of pages | 12 |
| ISBN (Electronic) | 9781509065431 |
| DOIs | |
| Publication status | Published - 16 May 2017 |
| Event | 33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States Duration: 19 Apr 2017 → 22 Apr 2017 |
Conference
| Conference | 33rd IEEE International Conference on Data Engineering, ICDE 2017 |
|---|---|
| Country | United States |
| City | San Diego |
| Period | 19/04/17 → 22/04/17 |
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Most influential community search over large social networks. / Li, Jianxin; Wang, Xinjue; Deng, Ke; Yang, Xiaochun; Sellis, Timos; Yu, Jeffrey Xu.
Proceedings - 2017 IEEE 33rd International Conference on Data Engineering (ICDE). San Diego, California : IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 871-882 7930032.Research output: Chapter in Book/Conference paper › Conference paper
TY - GEN
T1 - Most influential community search over large social networks
AU - Li, Jianxin
AU - Wang, Xinjue
AU - Deng, Ke
AU - Yang, Xiaochun
AU - Sellis, Timos
AU - Yu, Jeffrey Xu
PY - 2017/5/16
Y1 - 2017/5/16
N2 - Detecting social communities in large social networks provides an effective way to analyze the social media users' behaviors and activities. It has drawn extensive attention from both academia and industry. One essential aspect of communities in social networks is outer influence which is the capability to spread internal information of communities to external users. Detecting the communities of high outer influence has particular interest in a wide range of applications, e.g., Ads trending analytics, social opinion mining and news propagation pattern discovery. However, the existing detection techniques largely ignore the outer influence of the communities. To fill the gap, this work investigates the Most Influential Community Search problem to disclose the communities with the highest outer influences. We firstly propose a new community model, maximal kr-Clique community, which has desirable properties, i.e., society, cohesiveness, connectivity, and maximum. Then, we design a novel tree-based index structure, denoted as C-Tree, to maintain the offline computed r-cliques. To efficiently search the most influential communities, we also develop four advanced index-based algorithms which improve the search performance of non-indexed solution by about 200 times. The efficiency and effectiveness of our solution have been extensively verified using six real datasets and a small case study.
AB - Detecting social communities in large social networks provides an effective way to analyze the social media users' behaviors and activities. It has drawn extensive attention from both academia and industry. One essential aspect of communities in social networks is outer influence which is the capability to spread internal information of communities to external users. Detecting the communities of high outer influence has particular interest in a wide range of applications, e.g., Ads trending analytics, social opinion mining and news propagation pattern discovery. However, the existing detection techniques largely ignore the outer influence of the communities. To fill the gap, this work investigates the Most Influential Community Search problem to disclose the communities with the highest outer influences. We firstly propose a new community model, maximal kr-Clique community, which has desirable properties, i.e., society, cohesiveness, connectivity, and maximum. Then, we design a novel tree-based index structure, denoted as C-Tree, to maintain the offline computed r-cliques. To efficiently search the most influential communities, we also develop four advanced index-based algorithms which improve the search performance of non-indexed solution by about 200 times. The efficiency and effectiveness of our solution have been extensively verified using six real datasets and a small case study.
UR - http://www.scopus.com/inward/record.url?scp=85021239199&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2017.136
DO - 10.1109/ICDE.2017.136
M3 - Conference paper
SP - 871
EP - 882
BT - Proceedings - 2017 IEEE 33rd International Conference on Data Engineering (ICDE)
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - San Diego, California
ER -