A graph matching method and a graph matching distance based on subgraph assignments
Pattern Recognition Letters
The Pólya information divergence
Information Sciences: an International Journal
Models and algorithms for computing the common labelling of a set of attributed graphs
Computer Vision and Image Understanding
Graph attribute embedding via Riemannian submersion learning
Computer Vision and Image Understanding
Mining large distributed log data in near real time
SLAML '11 Managing Large-scale Systems via the Analysis of System Logs and the Application of Machine Learning Techniques
Matching attributed graphs: 2nd-order probabilities for pruning the search tree
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Synthesis of median spectral graph
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Towards the unification of structural and statistical pattern recognition
Pattern Recognition Letters
Pattern analysis with graphs: Parallel work at Bern and York
Pattern Recognition Letters
Geometric graph comparison from an alignment viewpoint
Pattern Recognition
On the relation between the common labelling and the median graph
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
A comparison between structural and embedding methods for graph classification
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Graph database retrieval based on metric-trees
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Component retrieval based on a database of graphs for Hand-Written Electronic-Scheme Digitalisation
Expert Systems with Applications: An International Journal
Graph matching and clustering using kernel attributes
Neurocomputing
Towards the estimation of feature-based semantic similarity using multiple ontologies
Knowledge-Based Systems
Hi-index | 0.15 |
The notion of a random graph is formally defined. It deals with both the probabilistic and the structural aspects of relational data. By interpreting an ensemble of attributed graphs as the outcomes of a random graph, we can use its lower order distribution to characterize the ensemble. To reflect the variability of a random graph, Shannon's entropy measure is used. To synthesize an ensemble of attributed graphs into the distribution of a random graph (or a set of distributions), we propose a distance measure between random graphs based on the minimum change of entropy before and after their merging. When the ensemble contains more than one class of pattern graphs, the synthesis process yields distributions corresponding to various classes. This process corresponds to unsupervised learning in pattern classification. Using the maximum likelihood rule and the probability computed for the pattern graph, based on its matching with the random graph distributions of different classes, we can classify the pattern graph to a class.