Graph Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 72
- 보존된 평가 기록
- 808
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/4pygmalion/GAT_tensorflow
https://github.com/AaltoPML/Rethinking-pooling-in-GNNs
Hierarchical Graph Representation Learning with Differentiable Pooling
https://github.com/Aditya239233/GNNExplainer
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/AlexDuvalinho/GNNE_eval
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/Anak2016/GAT
https://github.com/AngusMonroe/GAT-pytorch
https://github.com/Anou9531/GAT
https://github.com/Arvidsmile/InductiveDialogueGraph
https://github.com/Aveek-Saha/Graph-Attention-Net
https://github.com/BIG-S2/keras-gnm
https://github.com/BamLubi/Paddle-SIGN
Detecting Beneficial Feature Interactions for Recommender Systems
https://github.com/BorgwardtLab/P-WL
https://github.com/CHFsky/GCC-paddle
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
https://github.com/ChengSashankh/trying-GAT
https://github.com/ChengyuSun/gat
https://github.com/CielAl/PatchySan
https://github.com/ColdenChan/GAT_D
https://github.com/Diego999/pyGAT
https://github.com/EverLookNeverSee/diag2model
https://github.com/Fedzbar/laser-release
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
https://github.com/FilippoMB/Pyramidal-Reservoir-Graph-Nerual-Networks
A Fair Comparison of Graph Neural Networks for Graph Classification
https://github.com/Graph-COM/GSAT
https://github.com/GraphSAINT/GraphSAINT
https://github.com/HamzaGbada/GCN-Numpy
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/HazyResearch/hgcn
https://github.com/HeapHop30/graph-attention-nets
Geometric deep learning on graphs and manifolds using mixture model CNNs
https://github.com/HeapHop30/hierarchical-pooling
https://github.com/HoganZhang/pygcn_python3
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/HongyangGao/Graph-U-Nets
https://github.com/INK-USC/MHGRN
https://github.com/INK-USC/RE-Net
https://github.com/IllinoisGraphBenchmark/IGB-Datasets
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/JIAQING-XIE/Fea2Fea
Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks
https://github.com/JamesChuanggg/ggnn.pytorch
https://github.com/JiabenLi/rgcn_paddlepaddle
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.