mlsanityData to 7 Oct 2026

Catch Causal Signals from Edges for Label Imbalance in Graph Classification

arXiv 2501.01707 ↗First posted 3 Jan 2025cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Despite significant advancements in causal research on graphs and its application to cracking label imbalance, the role of edge features in detecting the causal effects within graphs has been largely overlooked, leaving existing methods with untapped potential for further performance gains. In this paper, we enhance the causal attention mechanism through effectively leveraging edge information to disentangle the causal subgraph from the original graph, as well as further utilizing edge features to reshape graph representations. Capturing more comprehensive causal signals, our design leads to improved performance on graph classification tasks with label imbalance issues. We evaluate our approach on real-word datasets PTC, Tox21, and ogbg-molhiv, observing improvements over baselines. Overall, we highlight the importance of edge features in graph causal detection and provide a promising direction for addressing label imbalance challenges in graph-level tasks. The model implementation details and the codes are available on https://github.com/fengrui-z/ECAL

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 39 references that are in the map. First posted on arXiv 3 Jan 2025.

Builds on

20 of 39 references.

References · 20

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  4. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  5. 2019Strategies for Pre-training Graph Neural Networkscited by 964
  6. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  7. 2019Analyzing Learned Molecular Representations for Property Predictioncited by 373
  8. 2021GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networkscited by 147
  9. 2022Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphscited by 144
  10. 2022GOOD: A Graph Out-of-Distribution Benchmarkcited by 140
  11. 2021Causal Attention for Interpretable and Generalizable Graph Classificationcited by 108
  12. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
8 more
  1. 2021Generalizing Graph Neural Networks on Out-Of-Distribution Graphscited by 69
  2. 2024A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challengescited by 54
  3. 2023Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalizationcited by 29
  4. 2021Imbalanced Graph Classification via Graph-of-Graph Neural Networkscited by 27
  5. 2023Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalizationcited by 26
  6. 2022Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attackcited by 10
  7. 2023Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotioncited by 8
  8. 2023Towards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graphcited by 1

Built on by

Not cited yet.