mlsanityData to 14 Oct 2026

Self-organization Preserved Graph Structure Learning with Principle of Relevant Information

arXiv 2301.00015 ↗First posted 30 Dec 2022cs.LG cs.AIcited by 8

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Abstract (arXiv)

Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships. However, this fundamental assumption cannot always be satisfied, as real-world graphs are always incomplete, noisy, or redundant. How to reveal the inherent graph structure in a unified way remains under-explored. We proposed PRI-GSL, a Graph Structure Learning framework guided by the Principle of Relevant Information, providing a simple and unified framework for identifying the self-organization and revealing the hidden structure. PRI-GSL learns a structure that contains the most relevant yet least redundant information quantified by von Neumann entropy and Quantum Jensen-Shannon divergence. PRI-GSL incorporates the evolution of quantum continuous walk with graph wavelets to encode node structural roles, showing in which way the nodes interplay and self-organize with the graph structure. Extensive experiments demonstrate the superior effectiveness and robustness of PRI-GSL.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 24 of its 47 references that are in the map. First posted on arXiv 30 Dec 2022.

Builds on

24 of 47 references.

References · 24

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
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  5. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  6. 2018Pitfalls of Graph Neural Network Evaluationcited by 1,007
  7. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 826
  8. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 680
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  11. 2018Adversarial Attacks on Neural Networks for Graph Datacited by 397
  12. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 371
12 more
  1. 2020Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddingscited by 214
  2. 2018Adversarial Attack and Defense on Graph Data: A Surveycited by 167
  3. 2020Graph Information Bottleneck for Subgraph Recognitioncited by 119
  4. 2021SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networkscited by 110
  5. 2021Graph Structure Learning with Variational Information Bottleneckcited by 81
  6. 2021SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanismcited by 75
  7. 2014Layer aggregation and reducibility of multilayer interconnected networkscited by 34
  8. 2022Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashingcited by 31
  9. 2022Principle of Relevant Information for Graph Sparsificationcited by 9
  10. 2018Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applicationscited by 8
  11. 2019Multiscale Principle of Relevant Information for Hyperspectral Image Classificationcited by 2
  12. 2020Ego-based Entropy Measures for Structural Representationscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 0; 2026: 1.

8 papers.

  1. 2023Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classificationcited by 23
  2. 2023From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Modulecited by 19
  3. 2024DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashingcited by 16
  4. 2024DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learningcited by 14
  5. 2024GaGSL: Global-augmented Graph Structure Learning via Graph Information Bottleneckcited by 1
  6. 2024Probability Passing for Graph Neural Networks: Graph Structure and Representations Joint Learningcited by 0
  7. 2024Boolean Product Graph Neural Networkscited by 0
  8. 2026Differentiable Lifting for Topological Neural Networkscited by 0