Computer Science > Machine Learning
[Submitted on 17 Feb 2021 (v1), last revised 22 Apr 2021 (this version, v4)]
Title:Fast Graph Learning with Unique Optimal Solutions
View PDFAbstract: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 this http URL
Submission history
From: Sami Abu-El-Haija [view email][v1] Wed, 17 Feb 2021 02:00:07 UTC (82 KB)
[v2] Sun, 21 Feb 2021 14:39:03 UTC (75 KB)
[v3] Tue, 20 Apr 2021 08:43:54 UTC (215 KB)
[v4] Thu, 22 Apr 2021 09:32:50 UTC (215 KB)
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