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International Workshop on Mobility Analytics for Spatio-temporal and Social Data

MATES 2017: Mobility Analytics for Spatio-Temporal and Social Data pp 1–21Cite as

On Attributed Community Search

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10731))

Abstract

Communities, which are prevalent in attributed graphs (e.g., social networks and knowledge bases) can be used in emerging applications such as product advertisement and setting up of social events. Given a graph G and a vertex \(q \in G\), the community search (CS) query returns a subgraph of G that contains vertices related to q. In this article, we study CS over two common attributed graphs, where (1) vertices are associated with keywords; and (2) vertices are augmented with locations. For keyword-based attributed graphs, we investigate the keyword-based attributed community (or KAC) query, which returns a KAC for a query vertex. A KAC satisfies both structure cohesiveness (i.e., its vertices are tightly connected) and keyword cohesiveness (i.e., its vertices share common keywords). For spatial-based attributed graphs, we aim to find the spatial-aware community (or SAC), whose vertices are close structurally and spatially, for a query vertex in an online manner. To enable efficient KAC search and SAC search, we propose efficient query algorithms. We also perform experimental evaluation on large real datasets, and the results show that our methods achieve higher effectiveness than the state-of-the-art community retrieval algorithms. Moreover, our solutions are faster than baseline approaches. In addition, we develop the C-Explorer system to assist users in extracting, visualizing, and analyzing KACs.

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Notes

  1. 1.

    https://www.meetup.com/.

  2. 2.

    Without ambiguity, all the attributed graphs mentioned in this section refer to keyword-based attributed graphs.

  3. 3.

    All the proofs of lemmas in this article can be found in [13].

  4. 4.

    All the pseudocodes of algorithms in this article can be found in [13].

  5. 5.

    We use “node” to mean “CL-tree node” in Sect. 3.

  6. 6.

    For simplicity, in this section we call spatial-based attributed graphs spatial graphs.

  7. 7.

    To avoid ambiguity, we use word “node” for tree nodes in Sect. 4.

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Author information

Authors and Affiliations

  1. Department of Computer Science, The University of Hong Kong, Hong Kong, China

    Yixiang Fang & Reynold Cheng

Authors
  1. Yixiang Fang
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  2. Reynold Cheng
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Corresponding author

Correspondence to Yixiang Fang .

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Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Christos Doulkeridis

  2. University of Piraeus, Piraeus, Greece

    George A. Vouros

  3. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Qiang Qu

  4. Institute of Computing Technology, Beijing, China

    Shuhui Wang

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Fang, Y., Cheng, R. (2018). On Attributed Community Search. In: Doulkeridis, C., Vouros, G., Qu, Q., Wang, S. (eds) Mobility Analytics for Spatio-Temporal and Social Data. MATES 2017. Lecture Notes in Computer Science(), vol 10731. Springer, Cham. https://doi.org/10.1007/978-3-319-73521-4_1

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  • DOI: https://doi.org/10.1007/978-3-319-73521-4_1

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  • Online ISBN: 978-3-319-73521-4

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