Anchored Vertex Exploration for Community Engagement in Social Networks
Paper
Cai, Taotao, Li, Jianxin, Hasan Haldar, Nur Al, Mian, Ajmal, Yearwood, John and Sellis, Timos. 2020. "Anchored Vertex Exploration for Community Engagement in Social Networks
." 2020 IEEE 36th International Conference on Data Engineering (ICDE). Dallas, United States 20 - 24 Apr 2020 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/ICDE48307.2020.00042
| Paper/Presentation Title | Anchored Vertex Exploration for Community Engagement in Social Networks |
|---|---|
| Presentation Type | Paper |
| Authors | Cai, Taotao, Li, Jianxin, Hasan Haldar, Nur Al, Mian, Ajmal, Yearwood, John and Sellis, Timos |
| Journal or Proceedings Title | Proceedings of IEEE 36th International Conference on Data Engineering (ICDE) |
| Journal Citation | pp. 409-420 |
| Number of Pages | 12 |
| Year | 2020 |
| Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
| Place of Publication | United States |
| ISBN | 9781728129037 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/ICDE48307.2020.00042 |
| Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/9101364 |
| Web Address (URL) of Conference Proceedings | https://ieeexplore.ieee.org/xpl/conhome/9093725/proceeding |
| Conference/Event | 2020 IEEE 36th International Conference on Data Engineering (ICDE) |
| Event Details | 2020 IEEE 36th International Conference on Data Engineering (ICDE) Parent International Conference on Data Engineering Delivery In person Event Date 20 to end of 24 Apr 2020 Event Location Dallas, United States |
| Abstract | 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. |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 4605. Data management and data science |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Deakin University |
| University of Western Australia | |
| Swinburne University of Technology |
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