Graph indexing: a frequent structure-based approach
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Indexing Hierarchical Structures Using Graph Spectra
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Riemannian Framework for Tensor Computing
International Journal of Computer Vision
Clustering and Embedding Using Commute Times
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Flow Complexity: Fast Polytopal Graph Complexity and 3D Object Clustering
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Summarization graph indexing: beyond frequent structure-based approach
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
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
What is the complexity of a network? the heat flow-thermodynamic depth approach
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Information-Theoretic dissimilarities for graphs
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
Optimized dissimilarity space embedding for labeled graphs
Information Sciences: an International Journal
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In a previous work we have uncovered some of the most informative spectral features (Commute Times, Fiedler eigenvector, Perron-Frobenius eigenvector and Node Centrality) for graph discrimination. In this paper we propose a method which exploits information geometry (manifolds and geodesics) to characterize graphlets with covariance matrices involving the latter features. Once we have the vectorized covariance matrices in the tangent space each graph is characterized by a population of vectors in such space. Then we exploit bypass informationtheoretic measures for estimating the dissimilarities between populations of vectors. We test this measure in a very challenging database (GatorBait).