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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/yu-rp/dualprism
공식 코드Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classification
https://github.com/yuehaowang/sogcn
공식 코드https://github.com/yuelinan/codes-of-c2r
공식 코드Cooperative Classification and Rationalization for Graph Generalization
https://github.com/yuj-umd/graphRNN
공식 코드Learning Graph-Level Representations with Recurrent Neural Networks
https://github.com/yunshengb/GraphSim
공식 코드Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching
https://github.com/yuwvandy/g2gnn
공식 코드Imbalanced Graph Classification via Graph-of-Graph Neural Networks
https://github.com/zaixizhang/graphbackdoor
공식 코드https://github.com/zehong-wang/g2pm
공식 코드Scalable Graph Generative Modeling via Substructure Sequences
https://github.com/zehong-wang/gpm
공식 코드https://github.com/zehong-wang/simmlp
공식 코드https://github.com/zemingd/hybridpool4mixup
공식 코드On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing
https://github.com/zetayue/CPA
공식 코드Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation
https://github.com/zhangzhk0819/gnn-embedding-leaks
공식 코드https://github.com/zhengdao-chen/GNN4CD
공식 코드Supervised Community Detection with Line Graph Neural Networks
https://github.com/zhuergou/energy-transformer-for-graph-anomaly-detection
공식 코드https://github.com/zju-daily/gvex
공식 코드https://github.com/zjunet/G-Tuning
공식 코드Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns
https://github.com/zjzijielu/gnn-exp
공식 코드On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
https://github.com/zjzijielu/gnn-positional-structural-node-features
공식 코드On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
https://github.com/zshicode/gnn-attcl-protein
공식 코드Graph neural networks and attention-based CNN-LSTM for protein classification
https://github.com/zulunzhu/spikinggcn
공식 코드https://github.com/zwangeo/autogel
공식 코드AutoGEL: An Automated Graph Neural Network with Explicit Link Information
https://github.com/zysszy/preferential-labeling
공식 코드Generalized Equivariance and Preferential Labeling for GNN Node Classification
https://github.com/zzysonny/plane
공식 코드https://gitlab.com/cedric_sanders/masterarbeit
공식 코드https://gitlab.com/drti/swwl
공식 코드Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
https://github.com/1075225782/GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/2023-MindSpore-1/ms-code-11/tree/main/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/2023-MindSpore-1/ms-code-123
https://github.com/2023-MindSpore-1/ms-code-20
https://github.com/2023-MindSpore-1/ms-code-86
https://github.com/2023-MindSpore-4/Code12/tree/main/dengzhaohong/GFS_MindSpore-main
https://github.com/2023-MindSpore-4/Code8/tree/main/Alexnet
https://github.com/2024-MindSpore-1/Code5/tree/main/Alexnet
https://github.com/30Aniket/Graph-Convolutional-Networks-GCNs
Semi-Supervised Classification with Graph Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.