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Graph Property Prediction
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling
Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence
Graph Generative Pre-trained Transformer
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning
Data-Driven Self-Supervised Graph Representation Learning
Next Level Message-Passing with Hierarchical Support Graphs
Towards Neural Scaling Laws for Foundation Models on Temporal Graphs
Learning Long Range Dependencies on Graphs via Random Walks
Graph neural networks with configuration cross-attention for tensor compilers
Global Concept Explanations for Graphs by Contrastive Learning
Contextualized Messages Boost Graph Representations
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
Graph Transformers for Large Graphs
Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs
Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes
EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Learning Large Graph Property Prediction via Graph Segment Training
Graph Propagation Transformer for Graph Representation Learning
Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
A new perspective on building efficient and expressive 3D equivariant graph neural networks
Data-Centric Learning from Unlabeled Graphs with Diffusion Model
Graph Neural Networks Go Forward-Forward
Transformers Meet Directed Graphs
Transformers over Directed Acyclic Graphs
Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search
Affinity-Aware Graph Networks
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
Shortest Path Networks for Graph Property Prediction
An Empirical Study of Retrieval-enhanced Graph Neural Networks
Embedding Graphs on Grassmann Manifold
Recipe for a General, Powerful, Scalable Graph Transformer
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision
Structure-Aware Transformer for Graph Representation Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.