Implementing discrete mathematics: combinatorics and graph theory with Mathematica
Implementing discrete mathematics: combinatorics and graph theory with Mathematica
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On a relation between graph edit distance and maximum common subgraph
Pattern Recognition Letters
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
Machine Learning
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering and Embedding Using Commute Times
IEEE Transactions on Pattern Analysis and Machine Intelligence
New Acyclic and Star Coloring Algorithms with Application to Computing Hessians
SIAM Journal on Scientific Computing
Benchmarking GPUs to tune dense linear algebra
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Approximate graph edit distance computation by means of bipartite graph matching
Image and Vision Computing
A generative model for graph matching and embedding
Computer Vision and Image Understanding
Graph characteristics from the heat kernel trace
Pattern Recognition
Graph-Based Representations in Pattern Recognition and Computational Intelligence
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Accelerating large graph algorithms on the GPU using CUDA
HiPC'07 Proceedings of the 14th international conference on High performance computing
Solving path problems on the GPU
Parallel Computing
High-dimensional spectral feature selection for 3D object recognition based on reeb graphs
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Graph embedding using constant shift embedding
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Efficient Graph Comparison and Visualization Using GPU
CSE '11 Proceedings of the 2011 14th IEEE International Conference on Computational Science and Engineering
Manifold embedding of graphs using the heat kernel
IMA'05 Proceedings of the 11th IMA international conference on Mathematics of Surfaces
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Graph Characterization via Ihara Coefficients
IEEE Transactions on Neural Networks
| Hi-index | 0.10 |
Graph comparison algorithms based on metric space embedding have been proven robust in graph clustering and classification. In this paper we propose graph embedding method exploiting ordered invariants of distance k-graphs, which encode structure of shortest-paths. We study degree histograms of those graphs and use them to construct permutation invariant graph representation called vertex B-matrix. In order to extract more information from structural patterns we also define edge distance k-graphs and associated edge B-matrix. Next, several new graph characteristics obtained by condensing information stored in B-matrices are introduced. We demonstrate that our approach provides stable embedding, which captures relevant graph features. Experiments on classification with satellite photo and mutagenicity benchmark datasets revealed, that new descriptors allow for distinguishing graphs with non trivial structural differences. Moreover, they appear to outperform descriptors based on heat kernel matrix, being at the same time more effective computationally. In the end we test feature selection on B-matrices showing that selecting right B-submatrix can improve classification rate on testing datasets.