Multi-scale Attributed Node Embedding

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.

Code: https://github.com/iclr2020/MUSAE/

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:1909.13021/code)