Diffusion wavelet embedding: A multi-resolution approach for graph embedding in vector space

Hoda Bahonar

doi:10.1016/j.patcog.2017.09.030

Return

Diffusion wavelet embedding: A multi-resolution approach for graph embedding in vector space

2018-02-01

Hoda Bahonar *

Abdolreza Mirzaei

Richard C. Wilson

DOI：10.1016/j.patcog.2017.09.030

Original

Save

View PDF

Abstract

En 中文

In this article, we propose a multiscale method of embedding a graph into a vector space using diffusion wavelets. At each scale, we extract a detail subspace and a corresponding lower-scale approximation subspace to represent the graph. Representative features are then extracted at each scale to provide a scale-space description of the graph. The lower-scale is constructed using a super-node merging strategy based on nearest neighbor or maximum participation and the new adjacency matrix is generated using vertex identification. This approach allows the comparison of graphs where the important structural differences may be present at varying scales. Additionally, this method can improve the differentiating power of the embedded vectors and this property reduces the possibility of cospectrality typical in spectral methods, substantially. The experimental results show that augmenting the features of abstract levels to the graph features increases the graph classification accuracies in different datasets. (C) 2017 Published by Elsevier Ltd.

Keywords:

Spectral graph embedding

Diffusion wavelet

Multi-resolution analysis

Graph summarization

Scale space

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.