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/LUOyk1999/GNNPlus
공식 코드Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence
https://github.com/LUOyk1999/NodeID
공식 코드Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning
https://github.com/Lemon-cmd/energy-transformer-graph
공식 코드https://github.com/Lemon-cmd/energy-transformer-torch
공식 코드https://github.com/LeonResearch/iGCL
공식 코드https://github.com/Lihaogx/DNDN
공식 코드Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed Graphs
https://github.com/LingxiaoShawn/GLOD-Issues
공식 코드https://github.com/LiuChuang0059/graph-pooling-papers
공식 코드Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities
https://github.com/LourensT/DynamicNetworkSimulation
공식 코드https://github.com/MIRALab-USTC/GraphAKD
공식 코드Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation
https://github.com/MLAI-Yonsei/GPIO
공식 코드Graph Perceiver IO: A General Architecture for Graph Structured Data
https://github.com/MathieuCarriere/perslay
공식 코드PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures
https://github.com/MicheleUIT/Explaining_SGNN
공식 코드https://github.com/Microsoft/Graphormer
공식 코드Do Transformers Really Perform Bad for Graph Representation?
https://github.com/NSLab-CUK/S-CGIB
공식 코드https://github.com/Oxfordblue7/GCFL
공식 코드https://github.com/PKU-ML/canonicalization
공식 코드A Canonicalization Perspective on Invariant and Equivariant Learning
https://github.com/PV-Lab/AUTO-XRD
공식 코드https://github.com/PetarV-/GAT
공식 코드https://github.com/Puckett-Lab/explainability_CorticalSurfaceGDL
공식 코드An explainability framework for cortical surface-based deep learning
https://github.com/PurdueMINDS/RelationalPooling
공식 코드https://github.com/PurdueMINDS/size-invariant-GNNs
공식 코드Size-Invariant Graph Representations for Graph Classification Extrapolations
https://github.com/Qizhang-Feng/DEGREE
공식 코드DEGREE: Decomposition Based Explanation For Graph Neural Networks
https://github.com/RexYing/gnn-model-explainer
공식 코드GNNExplainer: Generating Explanations for Graph Neural Networks
https://github.com/RingBDStack/SUGAR
공식 코드https://github.com/RomanBresson/KAGNN
공식 코드https://github.com/Roy-Shubhajit/FIT-GNN
공식 코드https://github.com/RuihongQiu/FGNN
공식 코드Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks
https://github.com/Samyu0304/graph-information-bottleneck-for-Subgraph-Recognition
공식 코드https://github.com/Saro00/DGN
공식 코드https://github.com/THUDM/GCC
공식 코드GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
https://github.com/THUDM/cogdl
공식 코드https://github.com/USC-InfoLab/NeuroGNN
공식 코드Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data
https://github.com/UlrikFriisJensen/CHILI
공식 코드https://github.com/Unknown-Data/QGCN
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.