Papers with Code 아카이브
Graph Property Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 7
- 평가표
- 5
- 보존된 평가 기록
- 128
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing
A New Perspective on the Effects of Spectrum in Graph Neural Networks
Nested Graph Neural Networks
From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness
Geometric and Physical Quantities Improve E(3) Equivariant Message Passing
Global Self-Attention as a Replacement for Graph Convolution
Weisfeiler and Lehman Go Cellular: CW Networks
Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond
Do Transformers Really Perform Bad for Graph Representation?
How Attentive are Graph Attention Networks?
Do We Need Anisotropic Graph Neural Networks?
Parameterized Hypercomplex Graph Neural Networks for Graph Classification
RAN-GNNs: breaking the capacity limits of graph neural networks
E(n) Equivariant Graph Neural Networks
Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing
Identity-aware Graph Neural Networks
Directed Acyclic Graph Neural Networks
Breaking the Expressive Bottlenecks of Graph Neural Networks
Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification
Graph convolutions that can finally model local structure
Robust Optimization as Data Augmentation for Large-scale Graphs
Directional Graph Networks
Improving Graph Property Prediction with Generalized Readout Functions
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
Hierarchical Inter-Message Passing for Learning on Molecular Graphs
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
Wasserstein Embedding for Graph Learning
DeeperGCN: All You Need to Train Deeper GCNs
Principal Neighbourhood Aggregation for Graph Nets
How Powerful are Graph Neural Networks?
Hierarchical Graph Representation Learning with Differentiable Pooling
Graph Attention Networks
Semi-Supervised Classification with Graph Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.