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Graph Regression

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 22
보존된 평가 기록: 239

The regression task is similar to graph classification but using different loss function and performance metric.

코드

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

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

Geometric deep learning on graphs and manifolds using mixture model CNNs

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

Simplifying Graph Convolutional Networks

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

Graph Neural Networks with convolutional ARMA filters

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

Graph Attention Networks

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

How Powerful are Graph Neural Networks?

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

Geometric deep learning on graphs and manifolds using mixture model CNNs

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

OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

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

Simplifying Graph Convolutional Networks

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

Graph Attention Networks

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

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/sgc

Simplifying Graph Convolutional Networks

https://github.com/dongchen06/macacc

Neural Message Passing for Quantum Chemistry

https://github.com/dpstart/graphormer_new

Do Transformers Really Perform Bad for Graph Representation?

https://github.com/ds4dm/sGat

Graph Attention Networks

https://github.com/ds4dm/sparse-gcn

Graph Attention Networks

https://github.com/dzb1998/pyGAT

Graph Attention Networks

https://github.com/egyptdj/graph-neural-mapping

How Powerful are Graph Neural Networks?

https://github.com/ehejin/Pearl-PE

Learning Efficient Positional Encodings with Graph Neural Networks

https://github.com/emartinezs44/SparkGCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/erfmah/answering_graph_queries

Inductive Representation Learning on Large Graphs

https://github.com/fanzhenliu/dagad

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/fongyk/graph-attention

Graph Attention Networks

https://github.com/fredjo89/heterogeneous-mpnn

Neural Message Passing for Quantum Chemistry

https://github.com/gajeelSF/Reinforcement-Learning-Branch-and-Bound

Neural Message Passing for Quantum Chemistry

https://github.com/galkampel/HyperNetworks

Graph Attention Networks

https://github.com/gayanku/FDGATII

Graph Attention Networks

https://github.com/gbouritsas/gsn

Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting

https://github.com/gcucurull/jax-gat

Graph Attention Networks

https://github.com/giuseppefutia/link-prediction-code

Graph Attention Networks

https://github.com/gmum/umwpl2021

Graph Attention Networks

https://github.com/gordicaleksa/pytorch-GAT

Graph Attention Networks

https://github.com/gospodima/extended-simgnn

How Powerful are Graph Neural Networks?

https://github.com/graphcore/distributed-kge-poplar

OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

https://github.com/graphcore/ogb-lsc-pcqm4mv2

OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

https://github.com/guillaumejaume/tuto-dl-on-graphs

How Powerful are Graph Neural Networks?

https://github.com/hadarser/ProvablyPowerfulGraphNetworks

Provably Powerful Graph Networks

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