mlsanityData to 13 Oct 2026

Learning Graph Representations

arXiv 2102.02026 ↗First posted 3 Feb 2021cs.LG cs.SIcited by 5

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Abstract (arXiv)

Social and information networks are gaining huge popularity recently due to their various applications. Knowledge representation through graphs in the form of nodes and edges should preserve as many characteristics of the original data as possible. Some of the interesting and useful applications on these graphs are graph classification, node classification, link prediction, etc. The Graph Neural Networks have evolved over the last few years. Graph Neural Networks (GNNs) are efficient ways to get insight into large and dynamic graph datasets capturing relationships among billions of entities also known as knowledge graphs. In this paper, we discuss the graph convolutional neural networks graph autoencoders and spatio-temporal graph neural networks. The representations of the graph in lower dimensions can be learned using these methods. The representations in lower dimensions can be used further for downstream machine learning tasks.

Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). Builds on 20 of its 26 references that are in the map. First posted on arXiv 3 Feb 2021.

Builds on

20 of 26 references.

References · 20

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,318
  2. 2017Graph Attention Networkscited by 8,888
  3. 2017Inductive Representation Learning on Large Graphscited by 6,303
  4. 2018How Powerful are Graph Neural Networks?cited by 4,373
  5. 2013Auto-Encoding Variational Bayescited by 4,024
  6. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,024
  7. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,086
  8. 2016node2vec: Scalable Feature Learning for Networkscited by 1,915
  9. 2014DeepWalk: Online Learning of Social Representationscited by 1,544
  10. 2016Variational Graph Auto-Encoderscited by 1,379
  11. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,130
  12. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,101
8 more
  1. 2016Structured Sequence Modeling with Graph Convolutional Recurrent Networkscited by 274
  2. 2017CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filterscited by 203
  3. 2019Relational Pooling for Graph Representationscited by 150
  4. 2018Adversarially Regularized Graph Autoencoder for Graph Embeddingcited by 84
  5. 2020Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physicscited by 34
  6. 2020Simple and Effective Graph Autoencoders with One-Hop Linear Modelscited by 17
  7. 2019Keep It Simple: Graph Autoencoders Without Graph Convolutional Networkscited by 14
  8. 2020Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networkscited by 7

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 2; 2026: 1.

5 papers.

  1. 2026mHC-GNN: Manifold-Constrained Hyper-Connections for Graph Neural Networkscited by 4
  2. 2023IndoorGNN: A Graph Neural Network based approach for Indoor Localization using WiFi RSSIcited by 3
  3. 2022eBIM-GNN : Fast and Scalable energy analysis through BIMs and Graph Neural Networkscited by 0
  4. 2025QGraphLIME - Explaining Quantum Graph Neural Networkscited by 0
  5. 2025QGShap: Quantum Acceleration for Faithful GNN Explanationscited by 0