Graph simplification and matching using commute times
Pattern Recognition
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
Graph similarity using interfering quantum walks
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
The RW2 algorithm for exact graph matching
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Commute times for graph spectral clustering
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Evolving spanning trees using the heat equation
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Spanning trees from the commute times of random walks on graphs
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Commute times, discrete green's functions and graph matching
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Towards unitary representations for graph matching
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Coined quantum walks lift the cospectrality of graphs and trees
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
| Hi-index | 0.00 |
In this paper we propose a graph matching algorithm which uses random walks to compute topological features for each node, in order to identify candidate pairs of corresponding nodes in the two graphs. The algorithm automatically adapts the number of topological features required to determine the exact match among the nodes. Even if the proposed technique is not guaranteed to provide an exact solution for all graphs, the experiments on a benchmark dataset show that it can outperform other state of the art algorithms with respect to the computational requirements. In fact, the proposed algorithm is polynomial in the number of graph nodes.