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 TypePaper
AuthorsCai, Taotao, Li, Jianxin, Hasan Haldar, Nur Al, Mian, Ajmal, Yearwood, John and Sellis, Timos
Journal or Proceedings TitleProceedings of IEEE 36th International Conference on Data Engineering (ICDE)
Journal Citationpp. 409-420
Number of Pages12
Year2020
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISBN9781728129037
Digital Object Identifier (DOI)https://doi.org/10.1109/ICDE48307.2020.00042
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/document/9101364
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/9093725/proceeding
Conference/Event2020 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
AbstractUser 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 ContentDoes not contain sensitive content
ANZSRC Field of Research 20204605. Data management and data science
Public NotesFiles associated with this item cannot be displayed due to copyright restrictions.
Byline AffiliationsDeakin University
University of Western Australia
Swinburne University of Technology
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