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Node Classification
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Node Classification is a machine learning task in graph-based data analysis, where the goal is to assign labels to nodes in a graph based on the properties of nodes and the relationships between them.
Node Classification models aim to predict non-existing node properties (known as the target property) based on other node properties. Typical models used for node classification consists of a large family of graph neural networks. Model performance can be measured using benchmark datasets like Cora, Citeseer, and Pubmed, among others, typically using Accuracy and F1.
<span style="color:grey; opacity: 0.6">( Image credit: Fast Graph Representation Learning With PyTorch Geometric )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Roy-Shubhajit/FIT-GNN
공식 코드https://github.com/RoyalVane/SEGCN
공식 코드Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning
https://github.com/SGDE2020/embedding_stability
공식 코드The Effects of Randomness on the Stability of Node Embeddings
https://github.com/SabanciParallelComputing/GOSH
공식 코드https://github.com/Saro00/DGN
공식 코드https://github.com/SitaoLuan/ACM-GNN
공식 코드https://github.com/SitaoLuan/When-Do-GNNs-Help
공식 코드https://github.com/SkBlaz/DNR
공식 코드Deep Node Ranking for Neuro-symbolic Structural Node Embedding and Classification
https://github.com/SsGood/NFGNN
공식 코드https://github.com/StatsDLMathsRecomSys/Inductive-representation-learning-on-temporal-graphs
공식 코드https://github.com/SukwonYun/LTE4G
공식 코드https://github.com/Synerise/cleora
공식 코드Cleora: A Simple, Strong and Scalable Graph Embedding Scheme
https://github.com/THUDM/GCC
공식 코드GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
https://github.com/THUDM/cogdl
공식 코드https://github.com/TUM-DAML/pprgo_tensorflow
공식 코드https://github.com/TachiChan/CatGCN
공식 코드CatGCN: Graph Convolutional Networks with Categorical Node Features
https://github.com/TaoShuchang/AdvImmune
공식 코드Adversarial Immunization for Certifiable Robustness on Graphs
https://github.com/ThirdAIResearch/BOLT_Benchmarks/tree/main/graph_learning
공식 코드https://github.com/TianXieUSC/GraphHop
공식 코드GraphHop: An Enhanced Label Propagation Method for Node Classification
https://github.com/TianxiangZhao/GraphSmote
공식 코드GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks
https://github.com/Tiiiger/SGC
공식 코드https://github.com/TobiasSkovgaardJepsen/relational-fusion-networks
공식 코드https://github.com/Uchman21/MLGW
공식 코드Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification
https://github.com/UlrikFriisJensen/CHILI
공식 코드https://github.com/UnHans/UnGSL
공식 코드https://github.com/VITA-Group/Unified-LTH-GNN
공식 코드A Unified Lottery Ticket Hypothesis for Graph Neural Networks
https://github.com/Vachik-Dave/Neural-Brane-Neural-Bayesian-Personalized-Ranking-for-Attributed-Network-Embedding
공식 코드Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding
https://github.com/VeritasYin/DEGNN_node_classification
공식 코드Revisiting graph neural networks and distance encoding from a practical view
https://github.com/WEIYanbin1999/GITA
공식 코드GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning
https://github.com/Wei9711/NegGCNs
공식 코드https://github.com/XinyiZ001/mSHINE
공식 코드https://github.com/Yangyeeee/SGAT
공식 코드https://github.com/YingtongDou/CARE-GNN
공식 코드Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
https://github.com/YoungTimmy/SAG
공식 코드Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning
https://github.com/YuWVandy/TDGNN
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.