Multi-scale Attributed Node Embedding

Understand

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.