Summary of Graph Out-of-distribution Generalization Via Causal Intervention, by Qitian Wu et al.
Graph Out-of-Distribution Generalization via Causal Intervention
by Qitian Wu, Fan Nie, Chenxiao Yang, Tianyi Bao, Junchi Yan
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the issue of out-of-distribution (OOD) generalization for graph neural networks (GNNs). OOD generalization refers to a model’s ability to perform well on new, unseen data when it has only been trained on a specific dataset. GNNs are particularly susceptible to performance degradation in OOD scenarios because they rely heavily on the intricate interconnections between nodes in a graph. The authors identify the root cause of this issue as latent confounding bias, which arises from the environment influencing both the node features and target labels. This bias causes GNNs to focus on environment-sensitive correlations rather than predictive relationships. To address this challenge, the authors introduce a new learning objective based on causal inference that incorporates an environment estimator and a mixture-of-expert GNN predictor. This approach can mitigate confounding bias in training data and improve generalization performance. The proposed method achieves up to 27.4% accuracy improvement over state-of-the-art models on graph OOD generalization benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand why graph neural networks (GNNs) don’t work well when they see new, different types of graphs. GNNs are great at recognizing patterns in graphs we’ve seen before, but they struggle to adapt to new situations. The problem is that the environment around the nodes in the graph can influence both what makes up the node and what the node’s job is (its label). This means that GNNs might be learning to recognize patterns based on the environment rather than actual relationships between nodes. To solve this, researchers have developed a new way of training GNNs that takes into account the confounding bias caused by the environment. This approach can help GNNs generalize better and perform well even when they see new types of graphs. |
Keywords
* Artificial intelligence * Generalization * Gnn * Inference
