Keyword-based Community Search in Bipartite Spatial-Social Networks (Technical Report)

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 $q$, community search in bipartite graphs involves identifying a group of users containing $q$, 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 $(\omega, \pi)\mbox{-}keyword\mbox{-}core$, named Keyword-based Community Search in Bipartite Spatial-Social Networks ($KCS\mbox{-}BSSN$). The $KCS\mbox{-}BSSN$ returns a tightly-knit community, significant social influence, minimal travel distance, and includes a $(\omega, \pi)\mbox{-}keyword\mbox{-}core$. To address the $KCS\mbox{-}BSSN$ problem, we have developed pruning methods that effectively filter out irrelevant users and points of interest. To improve query-answering efficiency, we have also proposed an indexing technique named the bipartite-spatial-social index. Our pruning techniques, and indexing approach, have proven effective and efficient through experiments with real and artificial data sets.