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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/name-is-what/nc2d
공식 코드https://github.com/namkyeong/afgrl
공식 코드https://github.com/naveed92/gcn_modularity
공식 코드Modularity Optimization as a Training Criterion for Graph Neural Networks
https://github.com/nellaker-group/placenta
공식 코드A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs
https://github.com/neoques/graph-posterior-network
공식 코드Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization
https://github.com/nerdslab/halfhop
공식 코드Half-Hop: A graph upsampling approach for slowing down message passing
https://github.com/nerdsresearch/boosting-multitask-learning-on-graphs
공식 코드Boosting Multitask Learning on Graphs through Higher-Order Task Affinities
https://github.com/networkslab/ckgconv
공식 코드https://github.com/neu-statsml-research/boosting-multitask-learning-on-graphs
공식 코드Boosting Multitask Learning on Graphs through Higher-Order Task Affinities
https://github.com/newiz430/bandana
공식 코드https://github.com/nhuang37/gnn_community_detection
공식 코드A Spectral Analysis of Graph Neural Networks on Dense and Sparse Graphs
https://github.com/nicknakis/hbdm
공식 코드A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations
https://github.com/nicknakis/hm-ldm
공식 코드A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted Networks
https://github.com/nicolas-hbt/versatile-embeddings
공식 코드Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE
https://github.com/nikhilnayak-debug/am220
공식 코드Graph Attention for Heterogeneous Graphs with Positional Encoding
https://github.com/niuchh/smp
공식 코드Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing
https://github.com/northeastern-datalab/factorized-graphs
공식 코드Factorized Graph Representations for Semi-Supervised Learning from Sparse Data
https://github.com/novicestone/hyperminer
공식 코드https://github.com/nslab-cuk/community-aware-graph-transformer
공식 코드Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures
https://github.com/nslab-cuk/connector
공식 코드Connector 0.5: A unified framework for graph representation learning
https://github.com/nslab-cuk/unified-graph-transformer
공식 코드https://github.com/nsssjss/mhgcn
공식 코드https://github.com/ntkien1904/DegFairGNN
공식 코드https://github.com/oj9040/GraphSAGE_RL
공식 코드https://github.com/oliver-lemke/entropy_aware_message_passing
공식 코드https://github.com/oliver-lloyd/kge_param_sens
공식 코드Assessing the Effects of Hyperparameters on Knowledge Graph Embedding Quality
https://github.com/oom-debugger/ijcai23
공식 코드A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks
https://github.com/opengsl/opengsl
공식 코드OpenGSL: A Comprehensive Benchmark for Graph Structure Learning
https://github.com/orion-wyc/gaga
공식 코드Label Information Enhanced Fraud Detection against Low Homophily in Graphs
https://github.com/ouy7han/s2gsl
공식 코드https://github.com/palash1992/DynamicGEM
공식 코드https://github.com/pallaksch/normprop
공식 코드https://github.com/papercodeeric/lfh
공식 코드Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs
https://github.com/pasalab/sagnas
공식 코드SA-GNAS: Seed Architecture Expansion for Efficient Large-scale Graph Neural Architecture Search
https://github.com/petergriffinjin/heterformer
공식 코드Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.