Multi-scale Attributed Node Embedding

Benedek Rozemberczki; Carl Allen; Rik Sarkar

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arXiv · 1909.13021

Multi-scale Attributed Node Embedding

Benedek Rozemberczki·Carl Allen·Rik Sarkar

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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 networks and web graphs.

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BibTeX RIS

Benedek Rozemberczki, Carl Allen, Rik Sarkar. 2019-09-28. Multi-scale Attributed Node Embedding. https://arxiv.org/abs/1909.13021

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