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/alexOarga/haiku-geometric/blob/main/haiku_geometric/nn/conv/gin_conv.py
https://github.com/alexOarga/haiku-geometric/blob/main/haiku_geometric/nn/conv/pna_conv.py
https://github.com/alge24/eigenpooling
https://github.com/alililia/ms_contrib/tree/main/ascend_gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/alililia/ms_contrib/tree/main/gpu_gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/anilakash/indkgc
https://github.com/anish-lu-yihe/SVRT-by-GAT
https://github.com/anshul3899/GNNExplainer-Experiments
GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/apat1n/LightGCN-Pytorch
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
https://github.com/aramiracle/GIUNet
https://github.com/arangoml/fastgraphml
https://github.com/arch4ngel21/scikit-fingerprints
Molecular Fingerprints Are Strong Models for Peptide Function Prediction
https://github.com/asarigun/GraphMixerNetworks
https://github.com/asif-salim/GCN-Graph_Convolutional_Network
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/asrtoufa/wikispeedia-paths-dual-hypergraph-features
https://github.com/aszot/ggnn
https://github.com/basiralab/RG-Select
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/basiralab/Reproducible-Generative-Learning
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/basiralab/reproduciblefedgnn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/bcsrn/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/bdqnghi/bi-tbcnn
https://github.com/benedekrozemberczki/CapsGNN
https://github.com/benedekrozemberczki/GAM
https://github.com/benedekrozemberczki/SEAL-CI
Semi-Supervised Graph Classification: A Hierarchical Graph Perspective
https://github.com/benedekrozemberczki/SimGNN
SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
https://github.com/benedekrozemberczki/benedekrozemberczki
https://github.com/benedekrozemberczki/graph2vec
https://github.com/bepping/non-oversmoothing-gcns
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/berlincho/RGCN-pytorch
https://github.com/birdwatcherYT/Learning-Interpretable-Metric-between-Graphs
https://github.com/bknyaz/graph_nn
Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules
https://github.com/blueberryc/pyGAT
https://github.com/bowu/GraphSearch
https://github.com/briangodwinlim/sir-gcn
https://github.com/calciver/Graph-Attention-Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.