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Efficient graph similarity join for information integration on graphs

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Abstract

Graphs have been widely used for complex data representation in many real applications, such as social network, bioinformatics, and computer vision. Therefore, graph similarity join has become imperative for integrating noisy and inconsistent data from multiple data sources. The edit distance is commonly used to measure the similarity between graphs. The graph similarity join problem studied in this paper is based on graph edit distance constraints. To accelerate the similarity join based on graph edit distance, in the paper, we make use of a preprocessing strategy to remove the mismatching graph pairs with significant differences. Then a novel method of building indexes for each graph is proposed by grouping the nodes which can be reached in k hops for each key node with structure conservation, which is the k-hop tree based indexing method. As for each candidate pair, we propose a similarity computation algorithm with boundary filtering, which can be applied with good efficiency and effectiveness. Experiments on real and synthetic graph databases also confirm that our method can achieve good join quality in graph similarity join. Besides, the join process can be finished in polynomial time.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Yue Wang, Hongzhi Wang, Jianzhong Li & Hong Gao

Authors
  1. Yue Wang
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  2. Hongzhi Wang
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  3. Jianzhong Li
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  4. Hong Gao
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Corresponding author

Correspondence to Hongzhi Wang.

Additional information

Yue Wang is a master student majoring in computer science and technology in Harbin Institute of Technology, China. Her main research area is XML data management.

Hongzhi Wang is an associate professor at Harbin Institute of Technology, China. His research area is data management, including data quality, XML data management and graph management. He is a recipient of the outstanding dissertation award of CCF, Microsoft Fellow and IBM PhD Fellowship.

Jianzhong Li is a professor and doctoral supervisor at Harbin Institute of Technology, China. He is a senior member of CCF. His research interests include database, parallel computing and wireless sensor networks, etc.

Hong Gao is a professor and doctoral supervisor at Harbin Institute of Technology, China. She is a senior member of CCF. Her research interests include data management, wireless sensor networks and graph database, etc.

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Wang, Y., Wang, H., Li, J. et al. Efficient graph similarity join for information integration on graphs. Front. Comput. Sci. 10, 317–329 (2016). https://doi.org/10.1007/s11704-015-4505-3

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