Papers with Code 아카이브

Graph Property Prediction

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/EricAlcaide/generalized-readout-phase

공식 코드

Improving Graph Property Prediction with Generalized Readout Functions

https://github.com/Graph-COM/GSAT

공식 코드

Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism

https://github.com/LUOyk1999/GNNPlus

공식 코드

Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence

https://github.com/Microsoft/Graphormer

공식 코드

Do Transformers Really Perform Bad for Graph Representation?

https://github.com/Optimization-AI/LibAUC

공식 코드

Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification

https://github.com/PetarV-/GAT

공식 코드

Graph Attention Networks

https://github.com/RBrossard/GINEPLUS

공식 코드

Graph convolutions that can finally model local structure

https://github.com/Saro00/DGN

공식 코드

Directional Graph Networks

https://github.com/UlrikFriisJensen/CHILI

공식 코드

CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning

https://github.com/ahmedesamy/dsgrl

공식 코드

Data-Driven Self-Supervised Graph Representation Learning

https://github.com/aimat-lab/megan_global_explanations

공식 코드

Global Concept Explanations for Graphs by Contrastive Learning

https://github.com/atomicarchitects/equiformer

공식 코드

Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs

https://github.com/atomicarchitects/equiformer_v2

공식 코드

EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

https://github.com/automl-research/pas-ogb

공식 코드

Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search

https://github.com/bayer-science-for-a-better-life/phc-gnn

공식 코드

Parameterized Hypercomplex Graph Neural Networks for Graph Classification

https://github.com/benjaminnNgo/ScalingTGNs

공식 코드

Towards Neural Scaling Laws for Foundation Models on Temporal Graphs

https://github.com/borgwardtlab/neuralwalker

공식 코드

Learning Long Range Dependencies on Graphs via Random Walks

https://github.com/borgwardtlab/sat

공식 코드

Structure-Aware Transformer for Graph Representation Learning

https://github.com/briangodwinlim/sir-gcn

공식 코드

Contextualized Messages Boost Graph Representations

https://github.com/carlosinator/support-graphs

공식 코드

Next Level Message-Passing with Hierarchical Support Graphs

https://github.com/conf20/egg

공식 코드

Embedding Graphs on Grassmann Manifold

https://github.com/czczup/gptrans

공식 코드

Graph Propagation Transformer for Graph Representation Learning

https://github.com/deepmind/digraph_transformer

공식 코드

Transformers Meet Directed Graphs

https://github.com/devnkong/FLAG

공식 코드

Robust Optimization as Data Augmentation for Large-scale Graphs

https://github.com/gbouritsas/graph-substructure-networks

공식 코드

Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting

https://github.com/gnnaskernel/gnnaskernel

공식 코드

From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness

https://github.com/goodfeli/adversarial

공식 코드

Generative Adversarial Networks

https://github.com/google-research-datasets/tpu_graphs

공식 코드

TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs

https://github.com/kaidic/gst

공식 코드

Learning Large Graph Property Prediction via Graph Segment Training

https://github.com/liugangcode/data_centric_transfer

공식 코드

Data-Centric Learning from Unlabeled Graphs with Diffusion Model

https://github.com/lukecavabarrett/pna

공식 코드

Principal Neighbourhood Aggregation for Graph Nets

https://github.com/mdeff/cnn_graph

공식 코드

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

https://github.com/muhanzhang/nestedgnn

공식 코드

Nested Graph Neural Networks

https://github.com/navid-naderi/WEGL

공식 코드

Wasserstein Embedding for Graph Learning

https://github.com/pyg-team/pytorch_geometric

공식 코드

Do We Need Anisotropic Graph Neural Networks?

https://github.com/qslim/epcb-gnns

공식 코드

Breaking the Expressive Bottlenecks of Graph Neural Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.