A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching by Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An energy function and continuous edit process for graph matching
Neural Computation
Error Correcting Graph Matching: On the Influence of the Underlying Cost Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
Replicator equations, maximal cliques, and graph isomorphism
Neural Computation
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
On Median Graphs: Properties, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A spectral approach to learning structural variations in graphs
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Geodesic entropic graphs for dimension and entropy estimation in manifold learning
IEEE Transactions on Signal Processing
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In this paper we propose a novel approach to obtain unambiguous and robust node attributes for matching non-attributed graphs. Such approach consists of exploiting the information coming from diffusion kernels to embed the subgraph induced by the neighborhood of each vertex in an Euclidean manifold and then use entropic graphs for measuring the α–entropy of the resulting distribution. Our experiments with random-generated graphs with 50 nodes show that at low edge densities, where the effect of structural noise is higher, this approach outperforms the description of the subgraph only in terms of diffusion kernels. Furthermore, our structural recognition experiments show that the approach has a practical application. All experiments were performed by weighting the well-known quadratic cost function used in the Softassign algorithm.