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Anchored vertex exploration for community engagement in social networks

conference contribution
posted on 2020-01-01, 00:00 authored by TaoTao Cai, Jianxin LiJianxin Li, Nur Al Hasan Haldar, Ajmal Mian, John YearwoodJohn Yearwood, Timos Sellis
User engagement has recently received significant attention in understanding decay and expansion of communities in social networks. However, the problem of user engagement hasn’t been fully explored in terms of users’ specific interests and structural cohesiveness altogether. Therefore, we fill the gap by investigating the problem of community engagement from the perspective of attributed communities. Given a set of keywords W, a structure cohesive parameter k, and a budget parameter l, our objective is to find l number of users who can induce a maximal expanded community. Meanwhile, every community member must contain the given keywords in W and the community should meet the specified structure cohesiveness constraint k. We introduce this problem as best-Anchored Vertex set Exploration (AVE).To solve the AVE problem, we develop a Filter-Verify framework by maintaining the intermediate results using multiway tree, and probe the best anchored users in a best search way. To accelerate the efficiency, we further design a keyword-aware anchored and follower index, and also develop an index-based efficient algorithm. The proposed algorithm can greatly reduce the cost of computing anchored users and their followers. Additionally, we present two bound properties that can guarantee the correctness of our solution. Finally, we demonstrate the efficiency of our proposed algorithms and index. We measure the effectiveness of attributed community-based community engagement model by conducting extensive experiments on five real-world datasets.



Data Engineering. International Conference (36th : 2020 : Dallas, Tex.)


409 - 420




Dallas, Tex.

Place of publication

Piscataway, N.J.

Start date


End date






Publication classification

E1 Full written paper - refereed



Title of proceedings

ICDE 2020 : Proceedings of the IEEE 36th International Conference on Data Engineering