Bahonar, Hoda, Mirzaei, Abdolreza and Wilson, Richard Charles orcid.org/0000-0001-7265-3033 (2017) Diffusion Wavelet Embedding: a Multi-resolution Approach for Graph Embedding in Vector Space. Pattern Recognition. pp. 1-37. ISSN 0031-3203
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Published by Elsevier Ltd.This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. |
Keywords: | Spectral graph embedding,diffusion wavelet,multi-resolution analysis,graph summarization,scale space |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 26 Sep 2017 16:15 |
Last Modified: | 02 Apr 2025 23:11 |
Published Version: | https://doi.org/10.1016/j.patcog.2017.09.030 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.1016/j.patcog.2017.09.030 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:121724 |