https://github.com/cf020031308/LinkDist
공식 코드Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
Papers with Code 아카이브
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/cf020031308/gem
공식 코드Graph Entropy Minimization for Semi-supervised Node Classification
https://github.com/cf020031308/gqn
공식 코드Unifying Label-inputted Graph Neural Networks with Deep Equilibrium Models
https://github.com/cf020031308/li-gnn
공식 코드Unifying Label-inputted Graph Neural Networks with Deep Equilibrium Models
https://github.com/changminwu/robustgcn
공식 코드Node Feature Kernels Increase Graph Convolutional Network Robustness
https://github.com/chaoshangcs/GTS
공식 코드Discrete Graph Structure Learning for Forecasting Multiple Time Series
https://github.com/charusharma1991/LinkPred
공식 코드Learning Representations using Spectral-Biased Random Walks on Graphs
https://github.com/chelsea234/lgpn
공식 코드Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network
https://github.com/chen-cai-osu/mpnn-gt-connection
공식 코드https://github.com/chen-gx/cie
공식 코드Causality and Independence Enhancement for Biased Node Classification
https://github.com/chennnM/GCNII
공식 코드https://github.com/chenzhaiyu/polygnn
공식 코드PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds
https://github.com/chrisbloecker/neuromap
공식 코드The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks
https://github.com/cityu-t/rcp-gnn
공식 코드Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal Training
https://github.com/cjpcool/PR-GAT
공식 코드https://github.com/cjpcool/customgnn
공식 코드https://github.com/cloudy1225/blnn
공식 코드Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised Learning
https://github.com/cmavro/grad
공식 코드Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
https://github.com/cods-gcs/kgnet
공식 코드https://github.com/cods-gcs/kgtosa
공식 코드Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling
https://github.com/colinneverland/staa
공식 코드Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning
https://github.com/cooperativenetworklearning/cooperative-network-learning
공식 코드Cooperative Network Learning for Large-Scale and Decentralized Graphs
https://github.com/cornell-zhang/polynormer
공식 코드Polynormer: Polynomial-Expressive Graph Transformer in Linear Time
https://github.com/cortys/gpn-extensions
공식 코드Linear Opinion Pooling for Uncertainty Quantification on Graphs
https://github.com/cripac-dig/getral
공식 코드Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks
https://github.com/cs-bio/torgnn
공식 코드https://github.com/cspjiao/RONE
공식 코드https://github.com/cuai/non-homophily-large-scale
공식 코드Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
https://github.com/currytang/llmgnn
공식 코드Label-free Node Classification on Graphs with Large Language Models (LLMS)
https://github.com/cy623/r2lp
공식 코드Resurrecting Label Propagation for Graphs with Heterophily and Label Noise
https://github.com/cycucas/gcniii
공식 코드https://github.com/cyh1112/GraphNormalization
공식 코드https://github.com/cynricfu/mecch
공식 코드MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks
https://github.com/czczup/gptrans
공식 코드Graph Propagation Transformer for Graph Representation Learning
https://github.com/daehoum1/pcfi
공식 코드Confidence-Based Feature Imputation for Graphs with Partially Known Features
https://github.com/daiquanyu/AdaGCN
공식 코드Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.