https://github.com/mashaan14/RPTree-GCN
공식 코드Random Projection Forest Initialization for Graph Convolutional Networks
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/matenure/FastGCN
공식 코드FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
https://github.com/matthew-hirn/magnet
공식 코드https://github.com/matthiaskohn/es-mlp
공식 코드https://github.com/maxwass/pygsl
공식 코드https://github.com/mayabechlerspeicher/tree-g
공식 코드https://github.com/mdeff/cnn_graph
공식 코드Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
https://github.com/meettyj/recipe2vec
공식 코드Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks
https://github.com/melopeo/GL
공식 코드Node Classification for Signed Social Networks Using Diffuse Interface Methods
https://github.com/mengcao327/attack-gnn-fairness
공식 코드Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks
https://github.com/mengchillee/SlimG
공식 코드Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining
https://github.com/mengchillee/neteffect
공식 코드NetEffect: Discovery and Exploitation of Generalized Network Effects
https://github.com/mengxang/quasi-framelet
공식 코드Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet Convolution
https://github.com/mengyingjiang/gcn-sa
공식 코드Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node Embedding
https://github.com/mengzaiqiao/SCAN
공식 코드https://github.com/metaoblivion/bandit_gnn_attack
공식 코드https://github.com/meteor0033/high-pass_gad
공식 코드High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel Approach
https://github.com/meteoswiss/peakweather
공식 코드PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning
https://github.com/mgithubl/mnci
공식 코드https://github.com/michailchatzianastasis/goat
공식 코드https://github.com/microsoft/ppgnn
공식 코드A Piece-wise Polynomial Filtering Approach for Graph Neural Networks
https://github.com/migalkin/NodePiece
공식 코드NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs
https://github.com/mingkai-zheng/simmatchv2
공식 코드https://github.com/minhzou/unig-encoder
공식 코드UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification
https://github.com/minjiyoon/cgt
공식 코드Graph Generative Model for Benchmarking Graph Neural Networks
https://github.com/mklab-iti/decentralized-gnn
공식 코드p2pGNN: A Decentralized Graph Neural Network for Node Classification in Peer-to-Peer Networks
https://github.com/mklabunde/on-the-prediction-instability-of-graph-neural-networks
공식 코드https://github.com/ml-research/cna_modules
공식 코드Graph Neural Networks Need Cluster-Normalize-Activate Modules
https://github.com/mlvlab/DeformableGCN
공식 코드https://github.com/mlvlab/SELAR
공식 코드Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs
https://github.com/mohammadesmaeili/GCNN
공식 코드Semi-Supervised Node Classification by Graph Convolutional Networks and Extracted Side Information
https://github.com/mr9812/joint-cluster-supervised-learning
공식 코드Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
https://github.com/mu-data-science/gaf
공식 코드A Scalable Tool For Analyzing Genomic Variants Of Humans Using Knowledge Graphs and Machine Learning
https://github.com/mustafaCoskunAgu/GArnoldi-GCN
공식 코드Generalized Learning of Coefficients in Spectral Graph Convolutional Networks
https://github.com/mustafaCoskunAgu/ScNP
공식 코드Graph Convolutional Networks Meet with High Dimensionality Reduction
https://github.com/myflashbarry/LLM-benchmarking
공식 코드Exploring Graph Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.