Papers with Code 아카이브

Graph Property Prediction

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

7

평가표

5

보존된 평가 기록

128

코드

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

https://github.com/qslim/gnn-spectrum

공식 코드

A New Perspective on the Effects of Spectrum in Graph Neural Networks

https://github.com/qslim/pdf

공식 코드

Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering

https://github.com/radoslav11/sp-mpnn

공식 코드

Shortest Path Networks for Graph Property Prediction

https://github.com/rampasek/GraphGPS

공식 코드

Recipe for a General, Powerful, Scalable Graph Transformer

https://github.com/robdhess/steerable-e3-gnn

공식 코드

Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

https://github.com/rusty1s/himp-gnn

공식 코드

Hierarchical Inter-Message Passing for Learning on Molecular Graphs

https://github.com/shamim-hussain/egt

공식 코드

Global Self-Attention as a Replacement for Graph Convolution

https://github.com/shamim-hussain/egt_pytorch

공식 코드

Global Self-Attention as a Replacement for Graph Convolution

https://github.com/shamim-hussain/egt_triangular

공식 코드

Global Self-Attention as a Replacement for Graph Convolution

https://github.com/shamim-hussain/tgt

공식 코드

Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers

https://github.com/shichangzh/gstarx

공식 코드

GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games

https://github.com/shyam196/egc

공식 코드

Do We Need Anisotropic Graph Neural Networks?

https://github.com/snap-research/largegt

공식 코드

Graph Transformers for Large Graphs

https://github.com/tech-srl/how_attentive_are_gats

공식 코드

How Attentive are Graph Attention Networks?

https://github.com/thanhhau097/google_fast_or_slow

공식 코드

Graph neural networks with configuration cross-attention for tensor compilers

https://github.com/tkipf/pygcn

공식 코드

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/toenshoff/CRaWl

공식 코드

Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing

https://github.com/torchmd/torchmd-net

공식 코드

TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials

https://github.com/twitter-research/cwn

공식 코드

Weisfeiler and Lehman Go Cellular: CW Networks

https://github.com/vgsatorras/egnn

공식 코드

E(n) Equivariant Graph Neural Networks

https://github.com/vthost/DAGNN

공식 코드

Directed Acyclic Graph Neural Networks

https://github.com/wdimmy/graphretrieval

공식 코드

An Empirical Study of Retrieval-enhanced Graph Neural Networks

https://github.com/weihua916/powerful-gnns

공식 코드

How Powerful are Graph Neural Networks?

https://github.com/xbfu/FedVN

공식 코드

Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning

https://github.com/yuanqidu/leftnet

공식 코드

A new perspective on building efficient and expressive 3D equivariant graph neural networks

https://github.com/1075225782/GCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/2023-MindSpore-1/ms-code-11/tree/main/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/2024-MindSpore-1/Code5/tree/main/APDrawingGAN

Generative Adversarial Networks

https://github.com/30Aniket/Graph-Convolutional-Networks-GCNs

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/404nofound/gat

Graph Attention Networks

https://github.com/4pygmalion/GAT_tensorflow

Graph Attention Networks

https://github.com/AaltoPML/Rethinking-pooling-in-GNNs

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/AlephZr/CE-GAN

Generative Adversarial Networks

https://github.com/AlexMGitHub/PokeGAN

Generative Adversarial Networks

https://github.com/AlexanderBogatko/TensorFlow_Keras_GAN

Generative Adversarial Networks

https://github.com/Anak2016/GAT

Graph Attention Networks

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