Keyword-based Community Search in Bipartite Spatial-Social Networks (Technical Report)
Kovan A. Bavi, Xiang Lian

TL;DR
This paper introduces a novel keyword-based community search method in bipartite spatial-social networks, focusing on identifying tightly-knit, influential communities with minimal travel distance using new indexing and pruning techniques.
Contribution
It proposes the $( ext{ω,π})$-keyword-core and develops pruning and indexing methods to efficiently find relevant communities in bipartite spatial-social networks.
Findings
Pruning methods effectively filter irrelevant users and POIs.
The bipartite-spatial-social index improves query efficiency.
Experiments validate the effectiveness and efficiency of the proposed approach.
Abstract
Several approaches have been recently proposed for community search in bipartite graphs. These methods have shown promising results in identifying communities in real-world bipartite networks, such as social and biological networks. Given a query user , community search in bipartite graphs involves identifying a group of users containing , with common characteristics or functions within a given bipartite graph. These problems are particularly challenging because bipartite graphs have two distinct sets of nodes, and community search algorithms must account for this structure. However, finding communities in keyword-based bipartite spatial-social networks has yet to be investigated enough. The spatial-social networks are naturally structured as bipartite graphs. Thus, this paper proposes a new community search problem in Bipartite spatial-social networks with a novel $(\omega,…
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