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Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks

arXiv 2011.07267 ↗First posted 14 Nov 2020cs.LGcited by 21

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Abstract (arXiv)

Self-supervised learning is currently gaining a lot of attention, as it allows neural networks to learn robust representations from large quantities of unlabeled data. Additionally, multi-task learning can further improve representation learning by training networks simultaneously on related tasks, leading to significant performance improvements. In this paper, we propose three novel self-supervised auxiliary tasks to train graph-based neural network models in a multi-task fashion. Since Graph Convolutional Networks are among the most promising approaches for capturing relationships among structured data points, we use them as a building block to achieve competitive results on standard semi-supervised graph classification tasks.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Nov 2020.

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Citing papers per year: 2021: 6; 2022: 6; 2023: 3; 2024: 5; 2025: 1.

21 papers.

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  1. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 443
  2. 2021Graph Self-Supervised Learning: A Surveycited by 289
  3. 2021Graph Contrastive Learning Automatedcited by 287
  4. 2021Self-supervised Learning on Graphs: Contrastive, Generative,or Predictivecited by 136
  5. 2022Graph Attention Multi-Layer Perceptroncited by 82
  6. 2022Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentationscited by 28
  7. 2023ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detectioncited by 23
  8. 2021Graph Neural Networks: Methods, Applications, and Opportunitiescited by 16
  9. 2023ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Promptcited by 12
  10. 2022Generative Subgraph Contrast for Self-Supervised Graph Representation Learningcited by 8
  11. 2024Exploring Correlations of Self-Supervised Tasks for Graphscited by 7
  12. 2022Link Prediction with Contextualized Self-Supervisioncited by 4
9 more
  1. 2024Global-Local Graph Neural Networks for Node-Classificationcited by 4
  2. 2021Graph Attention MLP with Reliable Label Utilizationcited by 3
  3. 2024HealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networkscited by 3
  4. 2021Ranking Micro-Influencers: a Novel Multi-Task Learning and Interpretable Frameworkcited by 2
  5. 2024Unifying Graph Contrastive Learning via Graph Message Augmentationcited by 2
  6. 2024Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detectioncited by 1
  7. 2022Graph Representation Learning via Aggregation Enhancementcited by 0
  8. 2023HAT-GAE: Self-Supervised Graph Auto-encoders with Hierarchical Adaptive Masking and Trainable Corruptioncited by 0
  9. 2025FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medical Image Classificationcited by 0