research-article

A fast projected fixed-point algorithm for large graph matching

Authors:

Cheng-Lin LiuAuthors Info & Claims

Pattern Recognition, Volume 60, Issue C

Pages 971 - 982

https://doi.org/10.1016/j.patcog.2016.07.015

Published: 01 December 2016 Publication History

Abstract

We propose a fast algorithm for approximate matching of large graphs. Previous graph matching algorithms suffer from high computational complexity and therefore do not have good scalability. By using a new doubly stochastic projection, for matching two weighted graphs of n nodes, our algorithm has time complexity only O ( n 3 ) per iteration and space complexity O ( n 2 ) . We proved that our algorithm converges at a super-logarithmic rate. Experiments on large synthetic and real graphs (over 1000 nodes) were conducted to evaluate the performance of various algorithms. Results show that due to its fast convergence, our algorithm is more than an order of magnitude faster than the previous state-of-the-art algorithms, while maintaining comparable accuracy in large graph matching. HighlightsLow time complexity O(n3)/iteration for two graphs of n nodes.Super-logarithm convergence guarantee.Large graph matching experiments.

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Cited By

Liu HScaglione AWai H(2024)Blind Graph Matching Using Graph SignalsIEEE Transactions on Signal Processing10.1109/TSP.2024.338284072(1766-1781)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1109/TSP.2024.3382840
Wang SLiu XLiu SJin JTu WZhu XZhu EKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Align then fusionProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3600696(5882-5895)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3600696
Shen BNiu QZhu S(2020)Fabricated Pictures Detection with Graph MatchingProceedings of the 2020 2nd Asia Pacific Information Technology Conference10.1145/3379310.3379330(46-51)Online publication date: 17-Jan-2020
https://dl.acm.org/doi/10.1145/3379310.3379330
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Published In

cover image Pattern Recognition

Pattern Recognition Volume 60, Issue C

December 2016

1076 pages

ISSN:0031-3203

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Copyright © Elsevier Ltd.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 December 2016

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Cited By

Liu HScaglione AWai H(2024)Blind Graph Matching Using Graph SignalsIEEE Transactions on Signal Processing10.1109/TSP.2024.338284072(1766-1781)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1109/TSP.2024.3382840
Wang SLiu XLiu SJin JTu WZhu XZhu EKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Align then fusionProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3600696(5882-5895)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3600696
Shen BNiu QZhu S(2020)Fabricated Pictures Detection with Graph MatchingProceedings of the 2020 2nd Asia Pacific Information Technology Conference10.1145/3379310.3379330(46-51)Online publication date: 17-Jan-2020
https://dl.acm.org/doi/10.1145/3379310.3379330
Wang TLing HLang CFeng S(2018)Graph Matching with Adaptive and Branching Path FollowingIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2017.276759140:12(2853-2867)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1109/TPAMI.2017.2767591
Lee D(2018)Near-rings on digital Hopf groupsApplicable Algebra in Engineering, Communication and Computing10.1007/s00200-017-0341-z29:3(261-282)Online publication date: 26-Dec-2018
https://dl.acm.org/doi/10.1007/s00200-017-0341-z
Liu ZWang JYang XFeng S(2017)LibCooptNeurocomputing10.1016/j.neucom.2016.11.048237:C(414-417)Online publication date: 10-May-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.11.048

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