Multi-scale Attributed Node Embedding
Authors: Benedek Rozemberczki, Carl Allen, Rik Sarkar
Keywords: network embedding, graph embedding, node embedding, network science, graph representation learning
TL;DR: We develop efficient multi-scale approximate attributed network embedding procedures with provable properties.
Abstract: We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram. Observations from neighborhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-scale approach (MUSAE).  Capturing attribute-neighborhood relationships over multiple scales is useful for a diverse range of applications, including latent feature identification across disconnected networks with similar attributes. We prove theoretically that matrices of node-feature pointwise mutual information are implicitly factorized by the embeddings. Experiments show that our algorithms are robust, computationally efficient and outperform comparable models on social, web and citation network datasets.