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Fast Graph Learning with Unique Optimal Solutions

arXiv 2102.08530 ↗First posted 17 Feb 2021cs.LG cs.MS cs.SIcited by 0

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Abstract (arXiv)

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 M, without explicitly computing 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

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 43 references that are in the map. First posted on arXiv 17 Feb 2021.

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25 of 43 references.

References · 25

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2017Inductive Representation Learning on Large Graphscited by 6,284
  5. 2018How Powerful are Graph Neural Networks?cited by 4,355
  6. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  7. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  8. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  9. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  10. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  11. 2019Simplifying Graph Convolutional Networkscited by 1,470
  12. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
13 more
  1. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  2. 2020Simple and Deep Graph Convolutional Networkscited by 857
  3. 2018Link Prediction Based on Graph Neural Networkscited by 829
  4. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 583
  5. 2019MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixingcited by 442
  6. 2020SIGN: Scalable Inception Graph Neural Networkscited by 283
  7. 2017Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2veccited by 197
  8. 2018Adaptive Sampling Towards Fast Graph Representation Learningcited by 180
  9. 2020Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Taskscited by 137
  10. 2014Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorizationcited by 21
  11. 2021Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learningcited by 12
  12. 2021Memory-Associated Differential Learningcited by 3
  13. 2017Fast, Warped Graph Embedding: Unifying Framework and One-Click Algorithmcited by 2

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