mlsanityData to 7 Oct 2026

Graph Feature Gating Networks

arXiv 2105.04493 ↗First posted 10 May 2021cs.LG cs.AIcited by 3

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Abstract (arXiv)

Graph neural networks (GNNs) have received tremendous attention due to their power in learning effective representations for graphs. Most GNNs follow a message-passing scheme where the node representations are updated by aggregating and transforming the information from the neighborhood. Meanwhile, they adopt the same strategy in aggregating the information from different feature dimensions. However, suggested by social dimension theory and spectral embedding, there are potential benefits to treat the dimensions differently during the aggregation process. In this work, we investigate to enable heterogeneous contributions of feature dimensions in GNNs. In particular, we propose a general graph feature gating network (GFGN) based on the graph signal denoising problem and then correspondingly introduce three graph filters under GFGN to allow different levels of contributions from feature dimensions. Extensive experiments on various real-world datasets demonstrate the effectiveness and robustness of the proposed frameworks.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 38 of its 51 references that are in the map. First posted on arXiv 10 May 2021.

Builds on

38 of 51 references.

References · 38

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26 more
  1. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  2. 2020Graph Contrastive Learning with Augmentationscited by 1,203
  3. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,199
  4. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  5. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
  6. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
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  8. 2015Gated Graph Sequence Neural Networkscited by 792
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  18. 2020When Does Self-Supervision Help Graph Convolutional Networks?cited by 121
  19. 2020A Unified View on Graph Neural Networks as Graph Signal Denoisingcited by 110
  20. 2020Self-supervised Learning on Graphs: Deep Insights and New Directioncited by 108
  21. 2021DIG: A Turnkey Library for Diving into Graph Deep Learning Researchcited by 76
  22. 2018GeniePath: Graph Neural Networks with Adaptive Receptive Pathscited by 66
  23. 2020Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studiescited by 42
  24. 2018Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networkscited by 30
  25. 2020Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designscited by 25
  26. 2019Gated Graph Convolutional Recurrent Neural Networkscited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025GraphCliff: Short-Long Range Gating for Modeling Critical Activity Changes Caused by Subtle Molecular Differencescited by 0
  2. 2025Hybrid-Domain Adaptative Representation Learning for Gaze Estimationcited by 0
  3. 2026DPD-Cancer: Explainable Graph-Based Deep Learning for Small Molecule Anti-Cancer Activity Predictioncited by 0