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  • Experimental Clarification of Some Issues in Subgraph Isomorphism Algorithms

    Author(s)
    Ren, Xuguang
    Wang, Junhu
    Franciscus, Nigel
    Stantic, Bela
    Griffith University Author(s)
    Wang, John
    Stantic, Bela
    Year published
    2018
    Metadata
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    Abstract
    Graph data is ubiquitous in many domains such as social network, bioinformatics, biochemical and image analysis. Finding subgraph isomorphism is a fundamental task in most graph databases and applications. Despite its NP-completeness, many algorithms have been proposed to tackle this problem in practical scenarios. Recently proposed algorithms consistently claimed themselves faster than previous ones, while the fairness of their evaluation is questionable due to query-set selections and algorithm implementations. Although there are some existing works comparing the performance of state-of-the-art subgraph isomorphism algorithms ...
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    Graph data is ubiquitous in many domains such as social network, bioinformatics, biochemical and image analysis. Finding subgraph isomorphism is a fundamental task in most graph databases and applications. Despite its NP-completeness, many algorithms have been proposed to tackle this problem in practical scenarios. Recently proposed algorithms consistently claimed themselves faster than previous ones, while the fairness of their evaluation is questionable due to query-set selections and algorithm implementations. Although there are some existing works comparing the performance of state-of-the-art subgraph isomorphism algorithms under the same query-sets and implementation settings, we observed there are still some important issues left unclear. For example, it remains unclear how those algorithms behave when dealing with unlabelled graphs. It is debatable that the number of embeddings of a larger query is smaller than that of a smaller query, which further challenges the remark that the time cost should decrease for a good algorithm when increasing the size of the queries. In this paper, we conducted a comprehensive evaluation of three of most recent subgraph algorithms. Through the analysis of the experiment results, we clarify those issues.
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    Conference Title
    INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2018, PT II
    Volume
    10752
    DOI
    https://doi.org/10.1007/978-3-319-75420-8_7
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/383730
    Collection
    • Conference outputs

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