A polynomial-time approximation algorithm for the permanent of a matrix with nonnegative entries
Journal of the ACM (JACM)
Simpler core vector machines with enclosing balls
Proceedings of the 24th international conference on Machine learning
Fast nearest neighbor retrieval for bregman divergences
Proceedings of the 25th international conference on Machine learning
Graph spectra as a systematic tool in computational biology
Discrete Applied Mathematics
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
Fitting the smallest enclosing bregman ball
ECML'05 Proceedings of the 16th European conference on Machine Learning
Entropy versus heterogeneity for graphs
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Information-geometric graph indexing from bags of partial node coverages
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Characterizing graphs using approximate von Neumann entropy
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Graph characterizations from von Neumann entropy
Pattern Recognition Letters
Heat flow-thermodynamic depth complexity in directed networks
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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In this paper we establish a formal link between network complexity in terms of Birkhoff-von Neumann decompositions and heat flow complexity (in terms of quantifying the heat flowing through the network at a given inverse temperature). We propose and proof characterization theorems and also two fluctuation laws for sets of networks. Such laws emerge from studying the implicacions of the Fluctuation Theorem in heat-flow characterization. Furthermore, we also define heat flow complexity in terms of thermodynamic depth, which results in a novel approach for characterizing networks and quantify their complexity In our experiments we characterize several protein-protein interaction (PPI) networks and then highlight their evolutive differences, in order to test the consistence of the proposed complexity measure in terms of the second law of thermodynamics.