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Using partial evaluation in holistic subgraph search

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Abstract

Because of its wide application, the subgraph matching problem has been studied extensively during the past decade. However, most existing solutions assume that a data graph is a vertex/edge-labeled graph (i.e., each vertex/edge has a simple label). These solutions build structural indices by considering the vertex labels. However, some real graphs contain rich-content vertices such as user profiles in social networks and HTML pages on the World Wide Web. In this study, we consider the problem of subgraph matching using a more general scenario. We build a structural index that does not depend on any vertex content. Based on the index, we design a holistic subgraph matching algorithm that considers the query graph as a whole and finds one match at a time. In order to further improve efficiency, we propose a “partial evaluation and assembly” framework to find subgraph matches over large graphs. Last but not least, our index has light maintenance overhead. Therefore, our method can work well on dynamic graphs. Extensive experiments on real graphs show that our method outperforms the state-of-the-art algorithms.

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Acknowledgements

This work was partially supported by the National Key Research and Development Program of China (2016YFB1000603), Fundamental Research Funds for the Central Universities, the National Natural Science Foundation of China (Grant Nos. 61622201, 61472131, and 61272546), and Science and Technology Key Projects of Hunan Province (2015 TP1004).

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Authors and Affiliations

  1. Big Data Provincial Key Laboratory, Hunan University, Changsha, 410082, China

    Peng Peng

  2. Institute of Computer Science and Technology, Peking University, Beijing, 100871, China

    Lei Zou, Zhenqin Du & Dongyan Zhao

Authors
  1. Peng Peng
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  2. Lei Zou
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  3. Zhenqin Du
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  4. Dongyan Zhao
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Corresponding author

Correspondence to Lei Zou.

Additional information

Peng Peng received his BS and PhD degrees in computer science from Beijing Normal University, China and Peking University, China in 2009 and 2016, respectively. He is currently an assistant professor at Hunan University, China. His research interests include graph databases and distributed database systems.

Lei Zou received his BS and PhD degrees in computer science from Huazhong University of Science and Technology, China in 2003 and 2009, respectively. He is currently an associate professor at Peking University, China. His research interests include graph databases and knowledge graph data management.

Zhenqin Du was an intern student in the Institute of Computer Science and Technology of Peking University (PKU), China. He joined the research project in the ICST of PKU concerning graph data management and knowledge graph application.

Dongyan Zhao received the BS, MS, and PhD degrees from Peking University (PKU), China in 1991, 1994, and 2000, respectively. He is currently a professor at PKU. His research interests are in information processing and knowledge management, including computer networks and intelligent agents.

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Peng, P., Zou, L., Du, Z. et al. Using partial evaluation in holistic subgraph search. Front. Comput. Sci. 12, 966–983 (2018). https://doi.org/10.1007/s11704-016-5522-6

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