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Most similar maximal clique query on large graphs

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Abstract

This paper studies the most similar maximal clique query (MSMCQ). Given a graph G and a set of nodes Q, MSMCQ is to find the maximal clique of G having the largest similarity with Q. MSMCQ has many real applications including advertising industry, public security, task crowdsourcing and social network, etc. MSMCQ can be studied as a special case of the general set similarity query (SSQ). However, the MCs of G has several specialties from the general sets. Based on the specialties of MCs, we propose a novel index, namely MCIndex. MCIndex outperforms the state-of-the-art SSQ method significantly in terms of the number of candidates and the query time. Specifically, we first construct an inverted index I for all the MCs of G. Since the MCs in a posting list often have a lot of overlaps, MCIndex selects some pivots to cluster the MCs with a small radius. Given a query Q, we compute the distance from the pivots to Q. The clusters of the pivots assured not answer can be pruned by our distance based pruning rule. Since it is NP-hard to construct a minimum MCIndex, we propose to construct a minimal MCIndex on I(v) with an approximation ratio 1 + ln |I(v)|. Since the MCs have properties that are inherent of graph structure, we further propose a SIndex within each cluster of a MCIndex and a structure based pruning rule. SIndex can significantly reduce the number of candidates. Since the sizes of intersections between Q and many MCs need to be computed during the query evaluation, we also propose a binary representation of MCs to improve the efficiency of the intersection size computation. Our extensive experiments confirm the effectiveness and efficiency of our proposed techniques on several real-world datasets.

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Acknowledgements

Thanks to the support of NSF of China (61502258, 61806105), Major Technology Innovation Project of Shandong (2018CXGC0703), NSF of Shandong Province (ZR2014FQ007), the Project of Shandong Finance Society (2018SDJR31), Soft Science Fund of Shandong Province (2018RKB01373, 2016RKB01043). Also thanks to the Scientific Research Start-up Fund and Outstanding Young Scholars Program of Social Science at Qilu University of Technology.

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Authors and Affiliations

  1. Research Center of Big Data Application, Qilu University of Technology, Jinan, 250353, China

    Yun Peng, Huawei Zhao & Zhizheng Zhou

  2. Data Science Institute, Columbia University, New York, 10027, USA

    Yitong Xu

  3. College of Mechanics and Materials, Hohai University, Nanjing, 210098, China

    Huimin Han

Authors
  1. Yun Peng
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  2. Yitong Xu
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  3. Huawei Zhao
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  4. Zhizheng Zhou
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Correspondence to Yun Peng.

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Yun Peng is an associate professor of the Research Center of Big Data Application, Qilu University of Technology, China. He received the PhD degree from the Hong Kong Baptist University, China in 2013 and received the BSci and MPhil degrees in computer science from Shandong University and the Harbin Institute of Technology (HIT), China in 2006 and 2008, respectively. His research interests include graph-structured data processing, data mining, and machine learning.

Yitong Xu is a data scientist at Amazon. He received his BA in math and statistics from the University of Minnesota, Twin Cities, USA and MS in data science from the Data Science Institute, Columbia University, USA. His research interests include information retrieval, graph data mining and recommendation system.

Huawei Zhao is a professor of the Qilu University of Technology, China. He received his MS in School of Computer Science, and PhD degrees in Institute of Information & Network Security from Shandong University, China in 2002 and 2006 respectively. His research interests include unstructured data processing, information security, and digital economy.

Zhizheng Zhou is an associate professor of Qilu University of Technology, China. His research fields include financial data analysis and FinTech.

Huimin Han is now a PhD student at the College of Mechanics and Materials, Hohai University, China. Her research interests include Big Data Analysis and Structural Health Monitoring.

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Peng, Y., Xu, Y., Zhao, H. et al. Most similar maximal clique query on large graphs. Front. Comput. Sci. 14, 143601 (2020). https://doi.org/10.1007/s11704-019-7235-0

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