ATLAS: Adaptive Topology-based Learning at Scale for Homophilic and Heterophilic Graphs
cs.LG
Submitted: 2025-12-16
Updated: 2026-08-26
Code: https://github.com/atlaspaper16/ATLAS
Terminology
Sources
- Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs
- Adaptive Universal Generalized PageRank Graph Neural Network
- Inductive Representation Learning on Large Graphs
- Open Graph Benchmark: Datasets for Machine Learning on Graphs
- A Survey on Explainability of Graph Neural Networks
- Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
- Revisiting Heterophily For Graph Neural Networks
- Improving Graph Neural Networks with Simple Architecture Design
- Geom-GCN: Geometric Graph Convolutional Networks
- Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond
- A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
- GNNs Getting ComFy: Community and Feature Similarity Guided Rewiring
- Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced training
- Graph Attention Networks
- A Comprehensive Survey on Graph Neural Networks
- A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
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