Abstract
Subgraph isomorphism is a common problem in several application fields where graphs are the best suited data representation, but it is known to be an NP-Complete problem. However, several algorithms exist that are fast enough on commonly encountered graphs so as to be practically usable; among them, for more than a decade VF2 has been the state of the art algorithm used to solve this problem and it is still the reference algorithm for many applications. Nevertheless, VF2 has been designed and implemented ten years ago when the structural features of the commonly used graphs were considerably different. Hence a renovation is required to make the algorithm able to compete in the challenges arisen in the last years, such as the use of graph matching on the very large graphs coming from bioinformatics applications. In this paper we propose a significant set of enhancements to the original VF2 algorithm that enable it to compete with more recently proposed graph matching techniques. Finally, we evaluate the effectiveness of these enhancement by comparing the matching performance both with the original VF2 and with several recent algorithms, using both the widely known MIVIA graph database and another public graph dataset containing real-world graphs from bioinformatics applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bonnici, V., Giugno, R., Pulvirenti, A., Shasha, D., Ferro, A.: A subgraph isomorphism algorithm and its application to biochemical data. BMC Bioinformatics 14 (2013)
Carletti, V., Foggia, P., Vento, M.: Performance Comparison of Five Exact Graph Matching Algorithms on Biological Databases. In: Petrosino, A., Maddalena, L., Pala, P. (eds.) ICIAP 2013. LNCS, vol. 8158, pp. 409–417. Springer, Heidelberg (2013)
Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in Pattern Recognition. IJPRAI 18(3), 265–298 (2004)
Cordella, L., Foggia, P., Sansone, C., Vento, M.: A (sub)graph isomorphism algorithm for matching large graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1367–1372 (2004)
Dahm, N., Bunke, H., Caelli, T., Gao, Y.: Efficient subgraph matching using topological node feature constraints. Pattern Recognition (June 2014)
Foggia, P., Percannella, G., Vento, M.: Graph Matching And Learning In Pattern Recognition On The Last Ten Years. …Journal of Pattern Recognition …(2014)
Han, W.S., Lee, J.H., Lee, J.H.: Turbo Iso: Towards Ultrafast And Robust Subgraph Isomorphism Search In Large Graph Databases. In: …of the 2013 International Conference on …, pp. 337–348 (2013)
He, H., Singh, A.K.: Graphs-At-A-Time: Query Language And Access Methods For Graph Databases. In: Proceedings of the 2008 ACM SIGMOD International …, pp. 405–417 (2008)
Huan, J., et al: Comparing graph representations of protein structure for mining family-specific residue-based packing motif. Journal of Computational Biology (2005)
Lacroix, V., Fernandez, C., Sagot, M.: Motif search in graphs: Application to metabolic networks. Transactions on computational biology and bioinformatics (Dicember 2006)
Larrosa, J., Valiente, G.: Constraint satisfaction algorithms for graph pattern matching. Mathematical Structures in Computer Science 12, 403–422 (2002)
McGregor, J.: Relational consistency algorithms and their application in finding subgraph and graph isomorphisms. Information Sciences 19(3), 229–250 (1979)
RCSB: Protein data bank web site (June 2015), http://www.rcsb.org/pdb
Shang, H., Zhang, Y., Lin, X., Yu, J.X.: Taming Verification Hardness: An Efficient Algorithm for Testing Subgraph Isomorphism, pp. 364–375 (2008)
Solnon, C.: Alldifferent-based filtering for subgraph isomorphism. Artificial Intelligence 174(12-13), 850–864 (2010)
Ullman, J.R.: An algorithm for subgraph isomorphism. J. Assoc. Comput. Mach. 23, 31–42 (1976)
Ullmann, J.: Bit-Vector Algorithms For Binary Constraint Satisfaction And Subgraph Isomorphism. Journal of Experimental Algorithmics (JEA) 15(1) (2010)
Vento, M.: A Long Tri. In: The Charming World Of Graphs For Pattern Recognition. Pattern Recognition, 1–11 (January 2014)
Vento, M., Jiang, X., Foggia, P.: International contest on pattern search in biological databases (June 2015), http://biograph2014.unisa.it
Zampelli, S., Deville, Y., Solnon, C.: Solving subgraph isomorphism problems with constraint programming. Constraints 15(3), 327–353 (2010)
Zhang, S., Li, S., Yang, J.: GADDI: Distance Index Based Subgraph Matching In Biological Networks. In: …of the 12th International Conference on …(2009)
Zhao, P., Han, J.: On Graph Query Optimization In Large Networks. Proceedings of the VLDB Endowment 3(1-2), 340–351 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Carletti, V., Foggia, P., Vento, M. (2015). VF2 Plus: An Improved version of VF2 for Biological Graphs. In: Liu, CL., Luo, B., Kropatsch, W., Cheng, J. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2015. Lecture Notes in Computer Science(), vol 9069. Springer, Cham. https://doi.org/10.1007/978-3-319-18224-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-18224-7_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18223-0
Online ISBN: 978-3-319-18224-7
eBook Packages: Computer ScienceComputer Science (R0)