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Efficient distributed subgraph similarity matching

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Abstract

Given a query graph \(q\) and a data graph \(G\), subgraph similarity matching is to retrieve all matches of \(q\) in \(G\) with the number of missing edges bounded by a given threshold \(\epsilon \). Many works have been conducted to study the problem of subgraph similarity matching due to its ability to handle applications involved with noisy or erroneous graph data. In practice, a data graph can be extremely large, e.g., a web-scale graph containing hundreds of millions of vertices and billions of edges. The state-of-the-art approaches employ centralized algorithms to process the subgraph similarity queries, and thus, they are infeasible for such a large graph due to the limited computational power and storage space of a centralized server. To address this problem, in this paper, we investigate subgraph similarity matching for a web-scale graph deployed in a distributed environment. We propose distributed algorithms and optimization techniques that exploit the properties of subgraph similarity matching, so that we can well utilize the parallel computing power and lower the communication cost among the distributed data centers for query processing. Specifically, we first relax and decompose \(q\) into a minimum number of sub-queries. Next, we send each sub-query to conduct the exact matching in parallel. Finally, we schedule and join the exact matches to obtain final query answers. Moreover, our workload-balance strategy further speeds up the query processing. Our experimental results demonstrate the feasibility of our proposed approach in performing subgraph similarity matching over web-scale graph data.

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Notes

  1. We assume \(q(v)>0\) in the following of this paper.

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Acknowledgments

This work is supported in part by the NSFC (Grant No. 61100024, 61332006, U1401256), the Fundamental Research Funds for the Central Universities (Grant No. N130504006), the National Basic Research Program of China (973, Grant No. 2011CB302200-G), the Research Grants Council of the Hong Kong SAR, China (Grant No. 14209314 and 418512), the NSFC (Grant No. 61328202), the Hong Kong RGC Project N HKUST637/13, the National Grand Fundamental Research 973 Program of China under Grant 2014CB340300, Microsoft Research Asia Gift Grant and Google Faculty Award 2013.

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Authors and Affiliations

  1. Northeastern University, Shenyang, China

    Ye Yuan & Guoren Wang

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

    Jeffery Yu Xu

  3. Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

    Lei Chen

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  1. Ye Yuan
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  2. Guoren Wang
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  3. Jeffery Yu Xu
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  4. Lei Chen
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Correspondence to Ye Yuan.

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Yuan, Y., Wang, G., Xu, J.Y. et al. Efficient distributed subgraph similarity matching. The VLDB Journal 24, 369–394 (2015). https://doi.org/10.1007/s00778-015-0381-6

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