An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Error Correcting Graph Matching: On the Influence of the Underlying Cost Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Linear time algorithm for isomorphism of planar graphs (Preliminary Report)
STOC '74 Proceedings of the sixth annual ACM symposium on Theory of computing
Constraint satisfaction algorithms for graph pattern matching
Mathematical Structures in Computer Science
A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate graph edit distance computation by means of bipartite graph matching
Image and Vision Computing
A correspondence measure for graph matching using the discrete quantum walk
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Model comparison with GenericDiff
Proceedings of the IEEE/ACM international conference on Automated software engineering
Similarity-Based Retrieval With Structure-Sensitive Sparse Binary Distributed Representations
Computational Intelligence
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In the field of structural pattern recognition, graphs provide us with a common and powerful way to represent objects. Yet, one of the main drawbacks of graph representation is that the computation of standard graph similarity measures is exponential in the number of involved nodes. Hence, such computations are feasible for small graphs only. The present paper considers the problem of graph isomorphism, i.e. checking two graphs for identity. A novel approach for the efficient computation of graph isomorphism is presented. The proposed algorithm is based on bipartite graph matching by means of Munkres' algorithm. The algorithmic framework is suboptimal in the sense of possibly rejecting pairs of graphs without making a decision. As an advantage, however, it offers polynomial runtime. In experiments on two TC-15 graph sets we demonstrate substantial speedups of our proposed method over several standard procedures for graph isomorphism, such as Ullmann's method, the VF2 algorithm, and Nauty. Furthermore, although the computational framework for isomorphism is suboptimal, we show that the proposed algorithm rejects only very few pairs of graphs.