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arXiv · 1912.02114

Keyword Aware Influential Community Search in Large Attributed Graphs

Md. Saiful Islam·Mohammed Eunus Ali·Yong-Bin Kang·Timos Sellis·Farhana M. Choudhury
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Abstract

We introduce a novel keyword-aware influential community query KICQ that finds the most influential communities from an attributed graph, where an influential community is defined as a closely connected group of vertices having some dominance over other groups of vertices with the expertise (a set of keywords) matching with the query terms (words or phrases). We first design the KICQ that facilitates users to issue an influential CS query intuitively by using a set of query terms, and predicates (AND or OR). In this context, we propose a novel word-embedding based similarity model that enables semantic community search, which substantially alleviates the limitations of exact keyword based community search. Next, we propose a new influence measure for a community that considers both the cohesiveness and influence of the community and eliminates the need for specifying values of internal parameters of a network. Finally, we propose two efficient algorithms for searching influential communities in large attributed graphs. We present detailed experiments and a case study to demonstrate the effectiveness and efficiency of the proposed approaches.

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Md. Saiful Islam, Mohammed Eunus Ali, Yong-Bin Kang, Timos Sellis, Farhana M. Choudhury. 2019-12-04. Keyword Aware Influential Community Search in Large Attributed Graphs. https://doi.org/10.1016/j.is.2021.101914

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