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/calciver/Graph-Attention-Networks

Graph Attention Networks

https://github.com/calciver/Graph-Isomorphism-Networks

How Powerful are Graph Neural Networks?

https://github.com/catalyst-team/gan

Generative Adversarial Networks

https://github.com/chandlersupple/GAN-in-Tensorflow

Generative Adversarial Networks

https://github.com/changminwu/expandergnn

Principal Neighbourhood Aggregation for Graph Nets

https://github.com/chappers/graph-differential-pooling

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/chenruduan/oareactdiff

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

https://github.com/chihchenghsieh/multimodal-medical-diagnosis-system

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

https://github.com/clin366/pygcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/code-implementation1/Code2/tree/main/APDrawingGAN

Generative Adversarial Networks

https://github.com/cvignac/SMP

Principal Neighbourhood Aggregation for Graph Nets

https://github.com/cybermonic/cage-4-submission

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dacostaHugo/AC-GANs

Generative Adversarial Networks

https://github.com/danielegrattarola/keras-gat

Graph Attention Networks

https://github.com/darnbi/pygcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/davidhalladay/CE-compressive-sensing-on-generative-model-pytorch

Generative Adversarial Networks

https://github.com/davidpicard/homm

Graph Attention Networks

https://github.com/daymos/simple_keras_GAN

Generative Adversarial Networks

https://github.com/ddehueck/pytorch-GAN

Generative Adversarial Networks

https://github.com/deathstar1/gan

Generative Adversarial Networks

https://github.com/deepchem/moleculenet

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dhrim/andong_2021

Generative Adversarial Networks

https://github.com/dhrim/cau_2021

Generative Adversarial Networks

https://github.com/dhrim/t-academy_2021

Generative Adversarial Networks

https://github.com/dhruvgrover1251/Playing_with_GANs

Generative Adversarial Networks

https://github.com/dineshzende/awesome-deep-learning-resources

Generative Adversarial Networks

https://github.com/divelab/good

Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism

https://github.com/dmeoli/neuro-sat

Graph Attention Networks

https://github.com/dmlc/dgl

Principal Neighbourhood Aggregation for Graph Nets

https://github.com/dmlc/dgl/blob/master/python/dgl/nn/pytorch/conv/gatv2conv.py

How Attentive are Graph Attention Networks?

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gin

How Powerful are Graph Neural Networks?

https://github.com/dmlc/dgl/tree/master/examples/pytorch/deepergcn

DeeperGCN: All You Need to Train Deeper GCNs

https://github.com/dmlc/dgl/tree/master/examples/pytorch/diffpool

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/dmlc/dgl/tree/master/examples/pytorch/gat

Graph Attention Networks

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