https://github.com/JiahaoWuGit/DcRec
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
Papers with Code 아카이브
Graph Classification
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Graph Classification is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.
<span style="color:grey; opacity: 0.6">( Image credit: Hierarchical Graph Pooling with Structure Learning )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Kaimaoge/IGNNK
https://github.com/Kennard123661/gcc
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
https://github.com/Ki-Seki/DGCNN-updated
https://github.com/KimMeen/GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/KrishnaswamyLab/LearnableScattering
https://github.com/Kristof-Neys/GAT_Neo4j_DGL
https://github.com/LarsHoldijk/RE-ParameterizedExplainerForGraphNeuralNetworks
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/LehengTHU/Agent4Rec
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
https://github.com/LeiCaiwsu/LGLP
https://github.com/LinJayan/SIGN-Paddle
Detecting Beneficial Feature Interactions for Recommender Systems
https://github.com/LouisDumont/GCN---re-implementation
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/MLDroid/graph2vec_tf
https://github.com/Mayurji/Image-Classification-PyTorch
https://github.com/MehmetBarutcu/Streaming-Algorithm-for-Monotone-k-Submodular-Maximization-with-Cardinality-Constraints
https://github.com/Messham87/COVID_GAT
https://github.com/MichSchli/RelationPrediction
https://github.com/Microsoft/gated-graph-neural-network-samples
https://github.com/Microsoft/graph-partition-neural-network-samples
https://github.com/MikelBros/Deep_Spammer_Detection_GCN
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
https://github.com/Mind23-2/MindCode-4/tree/main/gat
https://github.com/MindCode-4/code-10/tree/main/Alexnet-ABeffect
https://github.com/MindSpore-paper-code-3/code6/tree/main/Alexnet
https://github.com/MindSpore-scientific-2/code-12/tree/main/LGL-INR-master
https://github.com/MindSpore-scientific-2/code-4/tree/main/GR-HSTU
https://github.com/NMADALI97/French-web-domain-classification
https://github.com/Negus25/GAT-Mindspore
https://github.com/OpenXAIProject/GNNExplainer-Tutorial
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/PV-Lab/autoXRD
https://github.com/PaddlePaddle/PGL
https://github.com/PaddlePaddle/PGL/tree/main/ogb_examples/graphproppred/ogbg_molpcba
https://github.com/PaddlePaddle/PGL/tree/main/ogb_examples/nodeproppred/ogbn-arxiv/unimp_appnp_vnode_smooth
https://github.com/PaddlePaddle/PGL/tree/main/ogb_examples/nodeproppred/ogbn-proteins/unimp_cross_feat
https://github.com/PaddlePaddle/PGL/tree/master/examples/GaAN
https://github.com/PaddlePaddle/PGL/tree/master/ogb_examples/nodeproppred/ogbn-arxiv
https://github.com/PaddlePaddle/PaddleClas
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.