An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Enumerating all connected maximal common subgraphs in two graphs
Theoretical Computer Science
A Linear Programming Approach for the Weighted Graph Matching Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Eigenspace Projection Clustering Method for Inexact Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Common subgraph isomorphism detection by backtracking search
Software—Practice & Experience
Graph Matching using Spectral Embedding and Alignment
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Graph matching and clustering using spectral partitions
Pattern Recognition
Retrieval of objects in video by similarity based on graph matching
Pattern Recognition Letters
An Inexact Graph Comparison Approach in Joint Eigenspace
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A generative model for graph matching and embedding
Computer Vision and Image Understanding
Inexact Matching of Large and Sparse Graphs Using Laplacian Eigenvectors
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
A Path Following Algorithm for the Graph Matching Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise global alignment of protein interaction networks by matching neighborhood topology
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
A new approach and faster exact methods for the maximum common subgraph problem
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
High efficiency and quality: large graphs matching
The VLDB Journal — The International Journal on Very Large Data Bases
A novel model for medical image similarity retrieval
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Efficient geometric graph matching using vertex embedding
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Hi-index | 0.00 |
Graph matching plays an essential role in many real applications. In this paper, we study how to match two large graphs by maximizing the number of matched edges, which is known as maximum common subgraph matching and is NP-hard. To find exact matching, it cannot handle a graph with more than 30 nodes. To find an approximate matching, the quality can be very poor. We propose a novel two-step approach which can efficiently match two large graphs over thousands of nodes with high matching quality. In the first step, we propose an anchor-selection/expansion approach to compute a good initial matching. In the second step, we propose a new approach to refine the initial matching. We give the optimality of our refinement and discuss how to randomly refine the matching with different combinations. We conducted extensive testing using real and synthetic datasets, and will report our findings.