Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning Shape-Classes Using a Mixture of Tree-Unions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constructing basis functions from directed graphs for value function approximation
Proceedings of the 24th international conference on Machine learning
Simpler core vector machines with enclosing balls
Proceedings of the 24th international conference on Machine learning
Information Theory in Computer Vision and Pattern Recognition
Information Theory in Computer Vision and 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
Fitting the smallest enclosing bregman ball
ECML'05 Proceedings of the 16th European conference on Machine Learning
Graph characterizations from von Neumann entropy
Pattern Recognition Letters
Entropy and heterogeneity measures for directed graphs
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
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In this paper we extend the heat diffusion-thermodynamic depth approach for undirected networks/graphs to directed graphs. This extension is motivated by the need to measure the complexity of structural patterns encoded by directed graphs. It consists of: a) analyzing and characterizing heat diffusion traces in directed graphs, b) extending the thermodynamic depth framework to capture the second-order variability of the diffusion traces to measure the complexity of directed networks. In our experiments we characterize several directed networks derived from different natural languages. We show that our proposed extension finds differences between languages that are blind to the classical analysis of degree distributions.