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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/000Justin000/GBPN
공식 코드https://github.com/3sigmalab/MSGCN
공식 코드MSGCN: Multiplex Spatial Graph Convolution Network for Interlayer Link Weight Prediction
https://github.com/AI-luyuan/aegcn
공식 코드AEGCN: An Autoencoder-Constrained Graph Convolutional Network
https://github.com/AcEbt/CPL
공식 코드https://github.com/AdrianArnaiz/DiffWire
공식 코드https://github.com/AhmedBegggaUA/TFM
공식 코드Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters
https://github.com/AndreasLF/HowLowCanYouGo
공식 코드https://github.com/Ange1o/GPSP
공식 코드GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding
https://github.com/AnnonymousForPapers/GCN-proof
공식 코드https://github.com/ArchitParnami/Node2KG
공식 코드Transformation of Node to Knowledge Graph Embeddings for Faster Link Prediction in Social Networks
https://github.com/Axeln78/Transferability-of-spectral-gnns
공식 코드An Experimental Study of the Transferability of Spectral Graph Networks
https://github.com/Azadeh297/BiGTex
공식 코드Integrating Structural and Semantic Signals in Text-Attributed Graphs with BiGTex
https://github.com/BUPT-GAMMA/CPF
공식 코드https://github.com/BUPT-GAMMA/OpenHGNN
공식 코드Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks
https://github.com/BorealisAI/SLAPS-GNN
공식 코드SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks
https://github.com/BorgwardtLab/fisher_information_embedding
공식 코드https://github.com/CRIPAC-DIG/GCA
공식 코드https://github.com/CRIPAC-DIG/GET
공식 코드Evidence-aware Fake News Detection with Graph Neural Networks
https://github.com/CRIPAC-DIG/GRACE
공식 코드https://github.com/CRIPAC-DIG/GraphAIR
공식 코드GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
https://github.com/CRIPAC-DIG/H-GCN
공식 코드Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification
https://github.com/CRIPAC-DIG/RHGN
공식 코드https://github.com/CUAI/CorrectAndSmooth
공식 코드Combining Label Propagation and Simple Models Out-performs Graph Neural Networks
https://github.com/CUAI/Non-Homophily-Benchmarks
공식 코드https://github.com/ChandlerBang/AutoSSL
공식 코드https://github.com/ChandlerBang/Pro-GNN
공식 코드https://github.com/ChengtaiCao/Meta-GNN
공식 코드Meta-GNN: On Few-shot Node Classification in Graph Meta-learning
https://github.com/CheriseZhu/GIL
공식 코드https://github.com/ChristianKoke/HoloNets
공식 코드HoloNets: Spectral Convolutions do extend to Directed Graphs
https://github.com/CongWeilin/SGCN
공식 코드On the Importance of Sampling in Training GCNs: Tighter Analysis and Variance Reduction
https://github.com/CrawlScript/RpHGNN
공식 코드Efficient Heterogeneous Graph Learning via Random Projection
https://github.com/CrawlScript/tf_geometric
공식 코드Efficient Graph Deep Learning in TensorFlow with tf_geometric
https://github.com/CurryTang/Graph-LLM
공식 코드Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
https://github.com/DSE-MSU/DeepRobust
공식 코드https://github.com/Dandy5721/GNN-PDE-COV
공식 코드Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.