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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/labmlai/annotated_deep_learning_paper_implementations

Graph Attention Networks

https://github.com/laixinn/tgb

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

https://github.com/leejang/two_stream_ssd_caffe

SSD: Single Shot MultiBox Detector

https://github.com/lightaime/deep_gcns_torch

Training Graph Neural Networks with 1000 Layers

https://github.com/lightaime/deep_gcns_torch/tree/master/examples/ogb

Training Graph Neural Networks with 1000 Layers

https://github.com/lightaime/deep_gcns_torch/tree/master/examples/ogb_eff/ogbn_arxiv_dgl

DeeperGCN: All You Need to Train Deeper GCNs

https://github.com/lipingcoding/pygcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/liqiang2018/Vehicle_delector

SSD: Single Shot MultiBox Detector

https://github.com/liqiang2018/ssd_mobilenet1.5

SSD: Single Shot MultiBox Detector

https://github.com/liu6zijian/simplified-gcn-model

Graph Attention Networks

https://github.com/liuky74/caffe_liuky74

SSD: Single Shot MultiBox Detector

https://github.com/liuliu408/caffe-ssd

SSD: Single Shot MultiBox Detector

https://github.com/lswzjuer/caffe-adas

SSD: Single Shot MultiBox Detector

https://github.com/lt610/GraphSaint

GraphSAINT: Graph Sampling Based Inductive Learning Method

https://github.com/lucidrains/graph-transformer-pytorch

Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification

https://github.com/lufficc/SSD

SSD: Single Shot MultiBox Detector

https://github.com/lukecavabarrett/pna

Graph Attention Networks

https://github.com/lwhyc/oneshot_weaklyseg

Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images

https://github.com/lzx1413/CAFFE_SSD

SSD: Single Shot MultiBox Detector

https://github.com/machadoprx/GATv2-TensorFlow2

How Attentive are Graph Attention Networks?

https://github.com/macomino/TFM

SSD: Single Shot MultiBox Detector

https://github.com/mahendrathapa/graph-convolution-network

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/mahequn001/graphlearning/tree/master/model_zoo/gatv2

How Attentive are Graph Attention Networks?

https://github.com/mahequn001/graphlearning/tree/master/model_zoo/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/mahsa91/GKD

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/mahsa91/RA-GCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/manishsoni1908/mobilenet-ssd-keras

SSD: Single Shot MultiBox Detector

https://github.com/marble0117/GNN_models_pytorch

Graph Attention Networks

https://github.com/marble0117/GNN_models_pytorch_geometric

Graph Attention Networks

https://github.com/marblet/GNN_models_pytorch_geometric

Graph Attention Networks

https://github.com/marblet/gat-pytorch

Graph Attention Networks

https://github.com/masakicktashiro/rgcn_pytorch_implementation

Modeling Relational Data with Graph Convolutional Networks

https://github.com/massquantity/LibRecommender

Inductive Representation Learning on Large Graphs

https://github.com/maysambehmanesh/SGCL

GraphSAINT: Graph Sampling Based Inductive Learning Method

https://github.com/megvii-research/caddm

SSD: Single Shot MultiBox Detector

https://github.com/meliketoy/graph-cnn.pytorch

Semi-Supervised Classification with Graph Convolutional Networks

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