Recent results on heat kernel embedding of graphs

Authors:
Xiao Bai;Edwin R. Hancock
Affiliations:
Department of Computer Science, University of York, UK;Department of Computer Science, University of York, UK
Venue:
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Year:
2005

Citing 5
Cited 1

An Eigendecomposition Approach to Weighted Graph Matching Problems

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Spectral Algorithm for Seriation and the Consecutive Ones Problem

SIAM Journal on Computing
Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition

IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Graph Matching using Spectral Embedding and Alignment

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03

Graph Characteristics from the Ihara Zeta Function

SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes how heat-kernel asymptotics can be used to compute approximate Euclidean distances between nodes in a graph. The distances are used to embed the graph-nodes in a low-dimensional space by performing Multidimensional Scaling(MDS). We perform an analysis of the distances, and demonstrate that they are related to the sectional curvature of the connecting geodesic on the manifold. Experiments with moment invariants computed from the embedded points show that they can be used for graph clustering.