Learning Structural Node Embeddings Via Diffusion Wavelets

Understand

Nodes residing in different parts of a graph can have similar structural roles within their local network topology.

• The identification of such roles provides key insight into the organization of networks and can be used for a variety of machine learning tasks.
• However, learning structural representations of nodes is a challenging problem, and it has typically involved manually specifying and tailoring topological features for each node.
• In this paper, we develop GraphWave, a method that represents each node's network neighborhood via a low-dimensional embedding by leveraging heat wavelet diffusion patterns.