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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/daiquocnguyen/QGNN
공식 코드https://github.com/daiquocnguyen/Walk-Transformer
공식 코드https://github.com/dani3lwinter/cohop
공식 코드https://github.com/danielecastellana22/feature-structure-interplay-graph-learning
공식 코드Investigating the Interplay between Features and Structures in Graph Learning
https://github.com/danielecastellana22/lying-graph-convolution
공식 코드Lying Graph Convolution: Learning to Lie for Node Classification Tasks
https://github.com/danielegrattarola/spektral
공식 코드https://github.com/danielzuegner/gnn-meta-attack
공식 코드Adversarial Attacks on Graph Neural Networks via Meta Learning
https://github.com/daochenzha/simtsc
공식 코드https://github.com/daokunzhang/attri2vec
공식 코드https://github.com/daokunzhang/binaryne
공식 코드https://github.com/datake/AdaGCN
공식 코드AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models
https://github.com/davidlamprecht/autordf2gml
공식 코드AutoRDF2GML: Facilitating RDF Integration in Graph Machine Learning
https://github.com/dazhuoq/counterfactual_evidence
공식 코드https://github.com/deceptrax123/jpeb-gssl
공식 코드Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised Learning
https://github.com/deepgraphlearning/graphany
공식 코드https://github.com/deepgraphlearning/spn
공식 코드Neural Structured Prediction for Inductive Node Classification
https://github.com/deepkashiwa20/megacrn
공식 코드https://github.com/delta2323/gnn-asymptotics
공식 코드Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
https://github.com/devnkong/FLAG
공식 코드Robust Optimization as Data Augmentation for Large-scale Graphs
https://github.com/devnkong/VQ-GNN
공식 코드VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
https://github.com/dfdazac/blp
공식 코드Inductive Entity Representations from Text via Link Prediction
https://github.com/dfdazac/grapes
공식 코드GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
https://github.com/dglai/dgl-0.5-benchmark
공식 코드Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
https://github.com/dice-group/neuralclassexpressionsynthesis
공식 코드https://github.com/didi/hetsann
공식 코드An Attention-based Graph Neural Network for Heterogeneous Structural Learning
https://github.com/diegohuerta1/a-probabilistic-model-for-node-classification-in-directed-graphs
공식 코드A Probabilistic Model for Node Classification in Directed Graphs
https://github.com/dihuang0220/GraphEnsembleLearning
공식 코드https://github.com/divelab/DIG
공식 코드GraphFM: Improving Large-Scale GNN Training via Feature Momentum
https://github.com/divelab/DeeperGNN
공식 코드https://github.com/divelab/Non-Local-GNN
공식 코드https://github.com/divelab/lgcn
공식 코드https://github.com/divelab/sopool
공식 코드https://github.com/dmirlab-group/mgnn
공식 코드https://github.com/dmis-lab/hats
공식 코드HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
https://github.com/dms-net/scatteringGCN
공식 코드Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.