吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (2): 526-532.doi: 10.13229/j.cnki.jdxbgxb20170302

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Heuristic algorithm of all common subsequences of multiple sequences for measuring multiple graphs similarity

WANG Xu1, 2, OUYANG Ji-hong1, 2, CHEN Gui-fen3   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;
    3.College of Information Technology, Jilin Agricultural University, Changchun 130118, China
  • Received:2017-03-30 Online:2018-03-01 Published:2018-03-01

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Abstract: A heuristic algorithm A* for measuring the similarity of multiple graphs is proposed. In this algorithm, the multiple graphs are represented as multiple sequences, then the number of all common subsequences in the matches of the multiple graphs is calculated, and this number is used to measure the similarity of the multiple graphs. This algorithm avoids the redundant calculations in non-matches of multiple graphs, maximizes the heuristic function value of all common subsequences of the suffixes sequences, limits the search nodes to the subset of matches between two sequences, thus reducing the number of the calculation nodes. Compared with the existing graph similarity methods, the proposed algorithm can not only measure the similarity of multiple graphs, but also simplify the calculation process. The similarity of multiple graphs can be quickly measured using this algorithm with the boot of the heuristic information.

Key words: artificial intelligence, multiple graph similarity, heuristic algorithm, all common subsequences, multiple sequences, matches

CLC Number: 

  • TP18
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