Keyword-based Community Search in Bipartite Spatial-Social Networks (Technical Report)
It addresses a novel problem for users in spatial-social networks, such as social media or location-based services, by integrating keywords, spatial constraints, and bipartite structure, which is incremental as it builds on existing bipartite community search methods.
This paper tackles the problem of community search in keyword-based bipartite spatial-social networks by proposing a new problem called KCS-BSSN, which returns communities with tight connections, social influence, minimal travel distance, and a keyword-core, and demonstrates effectiveness through experiments on real and artificial datasets.
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 $(Ï, Ï)\mbox{-}keyword\mbox{-}core$, 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 $(Ï, Ï)\mbox{-}keyword\mbox{-}core$. 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.