Towards Causal Classification: A Comprehensive Study on Graph Neural Networks
Not on the map. Why?
Abstract (arXiv)
The exploration of Graph Neural Networks (GNNs) for processing graph-structured data has expanded, particularly their potential for causal analysis due to their universal approximation capabilities. Anticipated to significantly enhance common graph-based tasks such as classification and prediction, the development of a causally enhanced GNN framework is yet to be thoroughly investigated. Addressing this shortfall, our study delves into nine benchmark graph classification models, testing their strength and versatility across seven datasets spanning three varied domains to discern the impact of causality on the predictive prowess of GNNs. This research offers a detailed assessment of these models, shedding light on their efficiency, and flexibility in different data environments, and highlighting areas needing advancement. Our findings are instrumental in furthering the understanding and practical application of GNNs in diverse datacentric fields
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 48 references that are in the map. First posted on arXiv 27 Jan 2024.
Builds on
21 of 48 references.
References · 21
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2017Graph Attention Networkscited by 8,923
- 2017Inductive Representation Learning on Large Graphscited by 6,320
- 2018How Powerful are Graph Neural Networks?cited by 4,393
- 2019Graph Neural Networks for Social Recommendationcited by 714
- 2020Benchmarking Graph Neural Networkscited by 680
- 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 401
- 2019Position-aware Graph Neural Networkscited by 203
- 2020Data Augmentation for Graph Neural Networkscited by 203
- 2021Causal Attention for Interpretable and Generalizable Graph Classificationcited by 108
- 2021Sequential Recommendation with Graph Neural Networkscited by 89
- 2019DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classificationcited by 59
9 more
- 2021Simplifying approach to Node Classification in Graph Neural Networkscited by 56
- 2019STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systemscited by 54
- 2021Relating Graph Neural Networks to Structural Causal Modelscited by 41
- 2021Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Studycited by 36
- 2021Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Modelcited by 15
- 2023GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarkingcited by 11
- 2019Mutual Information Maximization in Graph Neural Networkscited by 9
- 2021Generalized Equivariance and Preferential Labeling for GNN Node Classificationcited by 7
- 2020Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximizationcited by 2
Built on by
Citing papers per year: 2025: 1.
1 paper.