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

PATTERN RECOGNITION

卷:74

Article

Bahonar, Hoda¹  Mirzaei, Abdolreza¹  Wilson, Richard C.² 

[1] Isfahan Univ Technol, Dept Elect & Comp Engn, Esfahan 8415683111, Iran

[2] Univ York, Dept Comp Sci, York, N Yorkshire, England

关键词: Spectral graph embedding;    Diffusion wavelet;    Multi-resolution analysis;    Graph summarization;    Scale space;   

DOI  :  10.1016/j.patcog.2017.09.030

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free