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arXiv · 2102.08530

Fast Graph Learning with Unique Optimal Solutions

Sami Abu-El-Haija·Valentino Crespi·Greg Ver Steeg·Aram Galstyan
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Abstract

We consider two popular Graph Representation Learning (GRL) methods: message passing for node classification and network embedding for link prediction. For each, we pick a popular model that we: (i) linearize and (ii) and switch its training objective to Frobenius norm error minimization. These simplifications can cast the training into finding the optimal parameters in closed-form. We program in TensorFlow a functional form of Truncated Singular Value Decomposition (SVD), such that, we could decompose a dense matrix $\mathbf{M}$, without explicitly computing $\mathbf{M}$. We achieve competitive performance on popular GRL tasks while providing orders of magnitude speedup. We open-source our code at http://github.com/samihaija/tf-fsvd

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Sami Abu-El-Haija, Valentino Crespi, Greg Ver Steeg, Aram Galstyan. 2021-02-17. Fast Graph Learning with Unique Optimal Solutions. https://arxiv.org/abs/2102.08530

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