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Community search over large semantic-based attribute graphs

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Abstract

Community search has attracted widespread attention in many fields, such as protein interaction networks, social networks, and knowledge graphs. It aims to find cohesive subgraphs that are closely related to a query vertex q in a graph G. Existing community search researches based on attribute graphs rarely consider the semantic information of attributes and interpretability of the community. In this paper, we study Community Search over Semantic-based Attribute Graphs (CSSAG), where the attribute of each vertex in the graph G is a semantic graph. To guarantee both the attribute and structure cohesiveness of the community, we introduce the maximal common subgraph and minimal degree metric to measure the cohesiveness of the attribute and structure, respectively. In this way, we can get more understandable and diverse cohesive subgraphs as depicted in the experiment. Also, we design three different online query algorithms by integrating new pruning strategies to shift the search space. Extensive experiments on real-world networks show that our approaches are effective and efficient.

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Notes

  1. http://networkrepository.com/fb-pages-politician.php

  2. https://www.flickr.com/

  3. http://dblp.uni-trier.de/xml/

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Acknowledgements

The research was supported by the National Key Research and Development Program Project of China (Grant No.2018YFB0204302), Key Area Research Program of Hunan (2019GK2091), Outstanding Youth of Department of Education of Hunan Province (18B483), and Zhejiang Lab (NO.2021KD0AB02).

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Authors and Affiliations

  1. College of Computer Science and Electronic Endineering, Hunan University, Changsha, 410082, China

    Peiying Lin, Xu Zhou, Peng Peng & Kenli Li

  2. College of Information Technology and Management, Hunan University of Finance and Economics, Changsha, 410000, China

    Siyang Yu

  3. College of Computer Science and Technology, National University of Defense Technology, Changsha, 410000, China

    Xiangke Liao

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  1. Peiying Lin
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  2. Siyang Yu
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  3. Xu Zhou
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Correspondence to Siyang Yu.

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This article belongs to the Topical Collection: Special Issue on Large Scale Graph Data Analytics

Guest Editors: Xuemin Lin, Lu Qin, Wenjie Zhang, and Ying Zhang

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Lin, P., Yu, S., Zhou, X. et al. Community search over large semantic-based attribute graphs. World Wide Web 25, 927–948 (2022). https://doi.org/10.1007/s11280-021-00942-y

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