GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning

arXiv:2509.23616 · cs.LG, cs.AI · Submitted 2025-09-28 · Read on arXiv

cs.LG, cs.AI

Submitted: 2025-09-28

Updated: 2026-09-15

Comments: PrePrint, 16 pages, 6 tables, 8 figures

Code: https://github.com/flzeng1/GraphIFE

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 44× in this paper · explained in 1 episode
• Generalization — 12× in this paper · explained in 13 episodes
• Alignment — 11× in this paper · explained in 1 episode
• Data Augmentation — 11× in this paper · explained in 7 episodes
• GNNs — 8× in this paper · explained in 2 episodes
• Wasserstein Distance — 8× in this paper · explained in 4 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Feature Extraction — 7× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Loss Function — 5× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Contrastive Learning — 3× in this paper · explained in 5 episodes
• Core — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Computational overhead — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Distribution Shift — 2× in this paper · explained in 6 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Generalizability — 2× in this paper · explained in 6 episodes
• Graph Neural Networks (GNNs) — 2× in this paper · explained in 7 episodes
• Objective Function — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• alpha — 2× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Causal Inference — 1× in this paper · explained in 4 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Dynamic Weight Averaging — 1× in this paper
• Fidelity — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• IMBENS: Ensemble Class-imbalanced Learning in Python
• Pitfalls of Graph Neural Network Evaluation
• Towards Out-Of-Distribution Generalization: A Survey
• Handling Distribution Shifts on Graphs: An Invariance Perspective

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