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

摘要

Highlights?Theabstract graphsof different levels are extracted through the proposed diffusion-wavelet-based graph summarization.?The abstract graphs are mapped into the approximation/detail subspaces using diffusion wavelet and form thes/d subgraphs.?The adjacency matrices of the reference graph and s/d subgraphs form theabstract subgraphs set.?The graph feature vector is formed by applying a selected base embedding method on the members of the abstract subgraphs set.?Two strategies are used for combining embedded vectors of the abstract subgraphs: the selected combination long vector and ensemble learning.?Using multiple embedded vectors for different subgraphs in a raw, suggests the proposed method as a good candidate for cospectrality reduction.?Utilizing diffusion wavelet, makes the extracted subgraphs of diffe