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

Highlights：

• The abstract graphs of 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 the s/d subgraphs.

• The adjacency matrices of the reference graph and s/d subgraphs form the abstract 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 different graphs comparable and this effect increases the classification accuracy.