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

Kovan A. Bavi, Xiang Lian

TL;DR

A new community search problem in Bipartite spatial-social networks with a novel $(\omega, \pi)\mbox{-}keyword\mbox{-}core$ is proposed, named Keyword-based Community Search in Bipartite Spatial-Social Networks ($KCS\mbox{-}BSSN), and an indexing technique named the bipartite-spatial-social index is proposed.

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 $(ω, π)\mbox{-}keyword\mbox{-}core$, 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 $(ω, π)\mbox{-}keyword\mbox{-}core$. 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.

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

TL;DR

A new community search problem in Bipartite spatial-social networks with a novel is proposed, named Keyword-based Community Search in Bipartite Spatial-Social Networks ($KCS\mbox{-}BSSN), and an indexing technique named the bipartite-spatial-social index is proposed.

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 , named Keyword-based Community Search in Bipartite Spatial-Social Networks (). The returns a tightly-knit community, significant social influence, minimal travel distance, and includes a . To address the 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.
Paper Structure (36 sections, 13 theorems, 23 equations, 5 figures, 3 tables, 3 algorithms)

This paper contains 36 sections, 13 theorems, 23 equations, 5 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Problem Definition
  3. Social Networks
  4. Spatial Road Networks
  5. Bipartite Spatial-Social Networks
  6. Keyword-Based Community Search Over Bipartite Spatial-Social Networks
  7. Challenges
  8. Pruning Techniques
  9. Keyword-Based Pruning
  10. omega-Based Pruning
  11. pi-Based Pruning
  12. Influence-Based Pruning
  13. Structural Cohesiveness Pruning
  14. Social-Distance-Based Pruning
  15. Spatial-Distance-Based Pruning
  16. ...and 21 more sections

Key Result

Lemma 1

(Keyword-Based Pruning). Given a bipartite graph $G_b$, a query keyword set $Q$, and a set, $V_p$, of POIs that user $u$ visited in $G_b$, user $u$ can be safely pruned, if $p.K \cap Q = \emptyset$ holds for all POIs $p \in V_p$, where $p.K$ is a keyword set associated with POI $p$.

Figures (5)

  • Figure 1: An example of bipartite spatial-social networks.
  • Figure 2: The $KCS\hbox{-}BSSN$ offline time vs. real/synthetic graph datasets.
  • Figure 3: The $KCS\hbox{-}BSSN$ performance vs. real/synthetic graph datasets.
  • Figure 4: Overall $KCS\hbox{-}BSSN$ performance comparison for different parameter settings.
  • Figure 5: The ablation study of our $KCS\hbox{-}BSSN$ pruning strategies on real/synthetic graphs, in terms of the pruning power.

Theorems & Definitions (23)

  • Example 1
  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Definition 5
  • Definition 6
  • Definition 7
  • Definition 8
  • Definition 9
  • ...and 13 more