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/chalothon/Graph-Convolutional-Networks-for-Relational-Link-Prediction
https://github.com/changminwu/expandergnn
https://github.com/chappers/graph-differential-pooling
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/chatterjeeayan/upna
https://github.com/chenfengzhao/gnnhls
https://github.com/chennnM/GCNII/tree/master/PyG/ogbn-arxiv
https://github.com/chfhf/GCC-paddle
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
https://github.com/chingyaoc/ggnn.pytorch
https://github.com/chrsmrrs/glocalwl
https://github.com/clin366/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/code-implementation1/Code2/tree/main/Alexnet
https://github.com/code-implementation1/Code9/tree/main/gat
https://github.com/compnet/pang
https://github.com/computerhistory/AlexNet-Source-Code
https://github.com/constantinosskitsas/Framework_GraphAlignment
https://github.com/crisbodnar/dgm
https://github.com/cvignac/SMP
https://github.com/cybermonic/cage-4-submission
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/danielegrattarola/keras-gat
https://github.com/dansuh17/alexnet-pytorch
https://github.com/darnbi/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/davidbuterez/edge-set-attention
An end-to-end attention-based approach for learning on graphs
https://github.com/davidpicard/homm
https://github.com/deepchem/moleculenet
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/demul/AlexNet
https://github.com/dglai/wsdm2022-challenge
https://github.com/diningphil/CGMM
A Fair Comparison of Graph Neural Networks for Graph Classification
https://github.com/diningphil/icgmm
A Fair Comparison of Graph Neural Networks for Graph Classification
https://github.com/dmeoli/neuro-sat
https://github.com/dmlc/dgl
https://github.com/dmlc/dgl/blob/master/python/dgl/nn/pytorch/conv/gatv2conv.py
https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat
https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/dmlc/dgl/tree/master/examples/mxnet/gin
https://github.com/dmlc/dgl/tree/master/examples/mxnet/graphsage
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.