https://github.com/wooden-spoon/relational-ERM
공식 코드Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data
Papers with Code 아카이브
Graph Sampling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Training GNNs or generating graph embeddings requires graph samples.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xhuang2016/gnn-benchmark
공식 코드Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glass
https://github.com/GraphSAINT/GraphACT
https://github.com/MindSpore-paper-code-2/code400/tree/main/Inception/inceptionv4
https://github.com/ZimpleX/gcn-ipdps19
https://github.com/acbull/pyHGT/tree/master/ogbn-mag
https://github.com/basiralab/falcon
https://github.com/dmlc/dgl/tree/master/examples/pytorch/graphsaint
https://github.com/dmlc/dgl/tree/master/examples/pytorch/hgt
https://github.com/dmlc/dgl/tree/master/examples/pytorch/sign
https://github.com/facebookresearch/NARS
https://github.com/hyeamykim/GCN-related-works
https://github.com/lightaime/deep_gcns_torch
https://github.com/lightaime/deep_gcns_torch/tree/master/examples/ogb
https://github.com/lightaime/deep_gcns_torch/tree/master/examples/ogb_eff/ogbn_arxiv_dgl
https://github.com/lt610/GraphSaint
https://github.com/maysambehmanesh/SGCL
https://github.com/syyunn/node2vec
https://github.com/thudm/graphmae2
https://github.com/wolfecameron/GIST
GIST: Distributed Training for Large-Scale Graph Convolutional Networks
https://github.com/xingsumq/us-defake
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.