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/valentingol/GANJax

Generative Adversarial Networks

https://github.com/vamsi3/simple-GAN

Generative Adversarial Networks

https://github.com/w28ahmad/FaceGen

Generative Adversarial Networks

https://github.com/wan2000/mc-mot

Graph Attention Networks

https://github.com/wayne1123/mnist_wgan_gp

Generative Adversarial Networks

https://github.com/weiyangfb/PyTorchSparseGAT

Graph Attention Networks

https://github.com/whut2962575697/gat_sementic_segmentation

Graph Attention Networks

https://github.com/willy-b/tiny-GIN-for-ogbg-molhiv

How Powerful are Graph Neural Networks?

https://github.com/xnuohz/DeeperGCN-dgl

DeeperGCN: All You Need to Train Deeper GCNs

https://github.com/xxayt/mindspore-GAN

Generative Adversarial Networks

https://github.com/yCobanoglu/infinite-width-gnns

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/yangjun1994/CAGCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/yangyucheng000/University/tree/main/model-2/graphormer

Do Transformers Really Perform Bad for Graph Representation?

https://github.com/yanhuchen/quantum-graph-convolutional-network

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/ydzhang-stormstout/HAT

Graph Attention Networks

https://github.com/ykrmm/sota_gnn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/yortyj/Exploiting-GANs-for-Phun-and-Profit

Generative Adversarial Networks

https://github.com/ytchx1999/GCN_res-FLAG

Robust Optimization as Data Augmentation for Large-scale Graphs

https://github.com/ytchx1999/Graphormer/tree/main/examples/ogb

Do Transformers Really Perform Bad for Graph Representation?

https://github.com/yuanxiaoyu0402/deep

Generative Adversarial Networks

https://github.com/yueqianlongma/GAN

Generative Adversarial Networks

https://github.com/yururyuru00/GAT_pyG

Graph Attention Networks

https://github.com/yuwvandy/g2gnn

How Powerful are Graph Neural Networks?

https://github.com/yzhuoning/DeepAUC_OGB_Challenge

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

https://github.com/zcemycl/Matlab-GAN

Generative Adversarial Networks

https://github.com/zeeshannisar/Generative-Models-Papers-with-Implementation-in-Keras/tree/master/GAN-Generative%20Adversarial%20Network

Generative Adversarial Networks

https://github.com/zhangbo2008/GAT_network

Graph Attention Networks

https://github.com/zhanglab-aim/cancer-net

Graph Attention Networks

https://github.com/zhao-tong/GNNs-easy-to-use

Graph Attention Networks

https://github.com/zhiqiang00/Hon-GCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/zhliping/Deep-Learning

How Powerful are Graph Neural Networks?

https://github.com/zxhhh97/ABot

Graph Attention Networks

https://gitlab.com/cedric_sanders/masterarbeit

Hierarchical Graph Representation Learning with Differentiable Pooling

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