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
[1] Hattori M, Okuno Y, Goto S, et al. Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways [J]. Journal of the American Chemical Society, 2003, 125(39): 11853-11865.
[2] Shanavas N, Wang H, Lin Z, et al. Supervised graph-based term weighting scheme for effective Text Classification [C] ∥ Proceedings of the 22nd European Conference on Artificial Intelligence, Hague, Netherlands, 2016: 1710-1711.
[3] Elzinga C, Wang H. Kernels for acyclic digraphs [J]. Pattern Recognition Letters, 2012, 33(16): 2239-2244.
[4] Sugiyama M, Llinares F, Kasenburg N, et al. Significant subgraph mining with multiple testing correction [C] ∥ Proceedings of the SIAM International Conference on Data Mining, Vancouver, Canada, 2015: 37-45.
[5] Li Tao, Dong Han, Shi Yong-tang, et al. A comparative analysis of new graph distance measures and graph edit distance [J]. Information Sciences, 2017, 403: 15-21.
[6] Zhu Gang-gao, Iglesias C. Computing semantic similarity of concepts in knowledge graphs [J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(1): 72-85.
[7] Sugiyama M, Borgwardt K. Halting in random walk Kernels [C]∥28th International Conference on Neural Information Processing Systems,Montreal, Canada, 2015: 1639-1647.
[8] Borgwardt K, Kriegel H. Shortest-path Kernels on graphs [C]∥Proceedings of IEEE International Conference on Data Mining, Houston, USA, 2005: 74-81.
[9] Szilagyi S, Szilagyi L. A fast hierarchical clustering algorithm for large-scale protein sequence data sets [J]. Computers in Biology and Medicine, 2014, 48: 94-101.
[10] Forster D, Bittner L, Karkar S, et al. Testing ecological theories with sequence similarity networks: marine ciliates exhibit similar geographic dispersal patterns as multicellular organisms [J]. BMC Biology, 2015,13(1):1-16.
[11] Yanardag P, Vishwanathan S. A structural smoothing framework for robust graph comparison [C]∥Proceedings of Neural Information Processing Systems, Montreal, Canada, 2015: 2134-2142.
[12] Wang H. All common subsequences [C]∥Proceeding 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, 2007: 635-640.
[13] Lin Z, Wang H, McClean S. A multidimensional sequence approach to measuring tree similarity [J]. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(2): 197-208.
[14] Chin F, Poon C.Performance analysis of some simple heuristics for computing longest common subsequences [J]. Algorithmica, 1994, 12: 293-311.
[15] Wang Q, Korkin D, Shang Y. A fast multiple longest common subsequence(MLCS) algorithm [J]. IEEE Transactions on Knowledge and Data Engineering, 2011, 23(3): 321-334.
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