Topology Based Scalable Graph Kernels
Machine Learning
2019-07-17 v1 Machine Learning
Abstract
We propose a new graph kernel for graph classification and comparison using Ollivier Ricci curvature. The Ricci curvature of an edge in a graph describes the connectivity in the local neighborhood. An edge in a densely connected neighborhood has positive curvature and an edge serving as a local bridge has negative curvature. We use the edge curvature distribution to form a graph kernel which is then used to compare and cluster graphs. The curvature kernel uses purely the graph topology and thereby works for settings when node attributes are not available.
Cite
@article{arxiv.1907.07129,
title = {Topology Based Scalable Graph Kernels},
author = {Kin Sum Liu and Chien-Chun Ni and Yu-Yao Lin and Jie Gao},
journal= {arXiv preprint arXiv:1907.07129},
year = {2019}
}