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Node Property Prediction

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

2

평가표

5

보존된 평가 기록

235

코드

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

https://github.com/shinkyuy/representation_learning_on_graphs_with_jumping_knowledge_networks

Representation Learning on Graphs with Jumping Knowledge Networks

https://github.com/shunk031/chainer-skin-lesion-detector

SSD: Single Shot MultiBox Detector

https://github.com/silent-code/deep-trace

Inductive Representation Learning on Large Graphs

https://github.com/snap-stanford/ogb/tree/master/examples/linkproppred/citation2

Open Graph Benchmark: Datasets for Machine Learning on Graphs

https://github.com/snap-stanford/ogb/tree/master/examples/linkproppred/collab

Open Graph Benchmark: Datasets for Machine Learning on Graphs

https://github.com/snap-stanford/ogb/tree/master/examples/linkproppred/ddi

Open Graph Benchmark: Datasets for Machine Learning on Graphs

https://github.com/snap-stanford/ogb/tree/master/examples/linkproppred/ppa

Open Graph Benchmark: Datasets for Machine Learning on Graphs

https://github.com/snap-stanford/ogb/tree/master/examples/nodeproppred/arxiv

Open Graph Benchmark: Datasets for Machine Learning on Graphs

https://github.com/snap-stanford/ogb/tree/master/examples/nodeproppred/mag

Open Graph Benchmark: Datasets for Machine Learning on Graphs

https://github.com/snap-stanford/ogb/tree/master/examples/nodeproppred/papers100M

Open Graph Benchmark: Datasets for Machine Learning on Graphs

https://github.com/snap-stanford/ogb/tree/master/examples/nodeproppred/products

Open Graph Benchmark: Datasets for Machine Learning on Graphs

https://github.com/snap-stanford/ogb/tree/master/examples/nodeproppred/proteins

Open Graph Benchmark: Datasets for Machine Learning on Graphs

https://github.com/snowkylin/gnn

Graph Attention Networks

https://github.com/snownus/COOP

Graph Attention Networks

https://github.com/spencerkraisler/Finger-Counter

SSD: Single Shot MultiBox Detector

https://github.com/stellargraph/stellargraph

Inductive Representation Learning on Large Graphs

https://github.com/stevensmiley1989/MrRobot

SSD: Single Shot MultiBox Detector

https://github.com/subercui/pyGConvAT

Graph Attention Networks

https://github.com/sunqiangxtcsun/SSD

SSD: Single Shot MultiBox Detector

https://github.com/sunxiaobei/mindspore_gallery/tree/main/grl/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/susurrant/flow-imputation

Modeling Relational Data with Graph Convolutional Networks

https://github.com/susurrant/spatial-interaction-modeling

Modeling Relational Data with Graph Convolutional Networks

https://github.com/syyunn/node2vec

Heterogeneous Graph Transformer

https://github.com/taishan1994/pytorch_gat

Graph Attention Networks

https://github.com/taokong/RON

SSD: Single Shot MultiBox Detector

https://github.com/tatp22/pytorch-fast-GAT

Graph Attention Networks

https://github.com/tatsuyaokunaga/Simpsons_Detection

SSD: Single Shot MultiBox Detector

https://github.com/tchernitski/caffe.deploy

SSD: Single Shot MultiBox Detector

https://github.com/tensorflow/gnn/blob/main/tensorflow_gnn/docs/api_docs/python/gnn/keras/layers/GATv2.md

How Attentive are Graph Attention Networks?

https://github.com/thanhtrunghuynh93/pygcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/thibaudmartinez/node2vec

node2vec: Scalable Feature Learning for Networks

https://github.com/thiviyanT/torch-rgcn

Modeling Relational Data with Graph Convolutional Networks

https://github.com/thudm/graphmae2

GraphSAINT: Graph Sampling Based Inductive Learning Method

https://github.com/timpostuvan/CTDG-link-anomaly-detection

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

https://github.com/timpostuvan/tgb-link-anomaly-detection-baselines

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

https://github.com/tk-rusch/graphcon

Semi-Supervised Classification with Graph Convolutional Networks

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