Characterization and graph embedding of weighted social networks through Diffusion Wavelets

More and more graph embedding algorithms have been proposed, which makes the similarity judgment of graph structure more and more accurate. While exploring the similarity of neighborhood structures, the existence of weights should also be taken into account, so as to reflect the relational social network graph in the real world. We use Graphwave, a kind of algorithms for graph embedding with diffusion wavelets, to incorporate weight into numerical value to calculate, and to process the returned probability distribution parameters, so that we can get some analysis about the actual complex network. Our analysis can overcome the priori misjudgment problem based on the topological structure, and then obtain the actual similarity of the network structure from the results of graph embedding.