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CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering Shape Classes using Tree Edit-Distance and Pairwise Clustering
International Journal of Computer Vision
A Riemannian approach to graph embedding
Pattern Recognition
A spectral approach to learning structural variations in graphs
Pattern Recognition
Graph embedding using tree edit-union
Pattern Recognition
Bayesian optimization of the scale saliency filter
Image and Vision Computing
Exact Median Graph Computation Via Graph Embedding
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Polytopal Graph Complexity, Matrix Permanents, and Embedding
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Graph characteristics from the heat kernel trace
Pattern Recognition
On the relation between the median and the maximum common subgraph of a set of graphs
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Graph embedding in vector spaces by means of prototype selection
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in 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
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In this paper we explore and compare two contrasting graph characterizations. The first of these is Estrada's heterogeneity index, which measures the heterogeneity of the node degree across a graph. Our second measure is the the von Neumann entropy associated with the Laplacian eigenspectrum of graphs. Here we show how to approximate the von Neumann entropy by replacing the Shannon entropy by its quadratic counterpart. This quadratic entropy can be expressed in terms of a series of permutation invariant traces, which can be computed from the node degrees in quadratic time. We compare experimentally the effectiveness of the approximate expression for the entropy with the heterogeneity index.