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An efficient method for top-k graph based node matching

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Abstract

Graph Pattern Matching (GPM) is to find those subgraphs that match a given pattern graph. In many applications, users are interested in thetop-k nodes that matches the label of a specific node, (named as the designated nodevd) included in a given pattern graph, rather than the entire set of matching. This is called Graph Pattern based Node Matching (GPNM) problem. However, the existing GPM methods for matching the designated nodevd in social graphs do not consider the social contexts like the social relationships, the social trust and the social positions which commonly exist in real applications, like the experts recommendation in social graphs, leading to deliver low quality designated nodes. In this paper, we first propose the conText-Aware Graph pattern based Top-K designed nodes finding problem (TAG-K), which involves the NP-Complete Multiple Constrained GPM problem, and thus it is NP-Complete. To address the efficiency and effectiveness issues of TAG-K in large-scale social graphs, we propose two indices, MA-Tree and SSC-Index, which can help efficiently find the Top-K matching. Furthermore, we propose a probabilistic algorithm based on the Monte Carlo Method, called MC-TAG-K. Based on the experimental results on five real social graphs, we have demonstrated that MC-TAG-K outperforms the existing methods in both efficiency and effectiveness.

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Authors and Affiliations

  1. Department of Computing, Macquarie University, Sydney, NSW, Australia

    Guanfeng Liu

  2. School of Computer Science and Technology, Soochow University, Suzhou Shi, China

    Qun Shi, An Liu & Zhixu Li

  3. School of Computer Science and Engineering and Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China

    Kai Zheng

  4. School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Australia

    Xiaofang Zhou

Authors
  1. Guanfeng Liu

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  2. Qun Shi

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  3. Kai Zheng

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  4. An Liu

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  5. Zhixu Li

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  6. Xiaofang Zhou

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Corresponding author

Correspondence toKai Zheng.

Additional information

This article belongs to the Topical Collection:Special Issue on Geo-Social Computing

Guest Editors: Guandong Xu, Wen-Chih Peng, Hongzhi Yin, Zi (Helen) Huang

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Liu, G., Shi, Q., Zheng, K.et al. An efficient method for top-k graph based node matching.World Wide Web22, 945–966 (2019). https://doi.org/10.1007/s11280-018-0577-y

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