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Key-core: cohesive keyword subgraph exploration in large graphs

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Abstract

Keyword search in graphs has been extensively studied in the literature. Given a keyword query, existing solutions mainly focus on finding all/top-k individual answers. Each individual answer is a subgraph/subtree that contains some structural information regarding a certain subset of nodes containing the keywords. Nevertheless, from the individually answers, it is difficult for a user to see the big picture and identify how the answers are correlated to each other. In this paper, we define a new structure, named key-core, to find cohesive subgraphs for a keyword query. Briefly speaking, a key-core is a cohesive subgraph that contains many highly correlated keyword search answers. A key-core is not only cohesive structurally, but also closely related to the user given keywords. In order to make the keyword search more flexible, we also define four key-operators, namely key-intersection, key-union, key-difference, and key-association, to manipulate the key-cores. The key-operators enable users to form complex queries and refine the queries on demand. We propose algorithms to compute the key-cores and key-operators efficiently. We conduct extensive performance studies on large real datasets to demonstrate the effectiveness and efficiency of our approach.

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Authors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Zhiwei Zhang, Guoren Wang & Ye Yuan

  2. The Chinese University of Hong Kong, Shatin, Hong Kong

    Jeffrey Xu Yu

  3. University of Electronic Science and Technology of China, Chengdu, China

    Lisi Chen

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  1. Zhiwei Zhang
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  2. Jeffrey Xu Yu
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  3. Guoren Wang
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  4. Ye Yuan
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  5. Lisi Chen
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Correspondence to Zhiwei Zhang.

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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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Zhang, Z., Yu, J.X., Wang, G. et al. Key-core: cohesive keyword subgraph exploration in large graphs. World Wide Web 25, 831–856 (2022). https://doi.org/10.1007/s11280-021-00926-y

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  • DOI: https://doi.org/10.1007/s11280-021-00926-y

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