Graph Property Prediction
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Benchmarks
Most implemented
Generative Adversarial Networks
How Attentive are Graph Attention Networks?
Do Transformers Really Perform Bad for Graph Representation?
E(n) Equivariant Graph Neural Networks
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
Recipe for a General, Powerful, Scalable Graph Transformer
Papers
TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs
Well-designed open-source software drives progress in Machine Learning (ML) research. While static graph ML enjoys mature frameworks like PyTorch Geometric and DGL, ML for temporal graphs (TG), networks that evolve over …
Graph Property PredictionGraph Positional Autoencoders as Self-supervised Learners
Graph self-supervised learning seeks to learn effective graph representations without relying on labeled data. Among various approaches, graph autoencoders (GAEs) have gained significant attention for their efficiency an…
Graph Property PredictionMissing ElementsNode ClassificationProperty Prediction+2Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling
The recent success of State-Space Models (SSMs) in sequence modeling has motivated their adaptation to graph learning, giving rise to Graph State-Space Models (GSSMs). However, existing GSSMs operate by applying SSM modu…
Computational EfficiencyGraph LearningGraph Property PredictionNode Classification+2GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks
Understanding complex three-dimensional (3D) structures of graphs is essential for accurately modeling various properties, yet many existing approaches struggle with fully capturing the intricate spatial relationships an…
Atomic ForcesComputational EfficiencyGraph Property PredictionGraph Regression+1Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence
Message-passing Graph Neural Networks (GNNs) are often criticized for their limited expressiveness, issues like over-smoothing and over-squashing, and challenges in capturing long-range dependencies, while Graph Transfor…
Graph ClassificationGraph Property PredictionGraph RegressionNode ClassificationGraph Generative Pre-trained Transformer
Graph generation is a critical task in numerous domains, including molecular design and social network analysis, due to its ability to model complex relationships and structured data. While most modern graph generative m…
Graph GenerationGraph Property PredictionPredictionProperty Prediction