https://github.com/ruizhang-ai/WGCN_Graph-Convolutional-Networks-with-Weighted-Structural-Features
공식 코드WGCN: Graph Convolutional Networks with Weighted Structural Features
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/russellizadi/ssp
공식 코드Optimization of Graph Neural Networks with Natural Gradient Descent
https://github.com/rusty1s/pytorch_geometric
공식 코드SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
https://github.com/safe-graph/DGFraud
공식 코드Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
https://github.com/safe-graph/RioGNN
공식 코드Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks
https://github.com/sajqavril/complement-laplacian-regularization
공식 코드Improving Your Graph Neural Networks: A High-Frequency Booster
https://github.com/sajqavril/feature-extension-graph-neural-networks
공식 코드https://github.com/samihaija/mixhop
공식 코드MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
https://github.com/samihaija/tf-fsvd
공식 코드https://github.com/samirmoustafa/mixq
공식 코드Efficient Mixed Precision Quantization in Graph Neural Networks
https://github.com/sannat17/csc413-project
공식 코드https://github.com/saper0/qpcert
공식 코드Exact Certification of (Graph) Neural Networks Against Label Poisoning
https://github.com/schmidt-ju/scene
공식 코드SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks
https://github.com/scottjiao/slotgat_icml23
공식 코드SlotGAT: Slot-based Message Passing for Heterogeneous Graph Neural Network
https://github.com/scylj1/gnn_edge_regression
공식 코드Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations
https://github.com/sebkaz/betastar
공식 코드https://github.com/seijimaekawa/a2dug
공식 코드A Simple and Scalable Graph Neural Network for Large Directed Graphs
https://github.com/seijimaekawa/empirical-study-of-gnns
공식 코드Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs
https://github.com/selgroup/dese
공식 코드https://github.com/sennadir/noisygnn
공식 코드A Simple and Yet Fairly Effective Defense for Graph Neural Networks
https://github.com/seonghyun26/nba-gnn
공식 코드https://github.com/seongjunyun/Graph_Transformer_Networks
공식 코드https://github.com/seongjunyun/neo_gnns
공식 코드Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction
https://github.com/seungyoon-choi/dslr_official
공식 코드DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual Learning
https://github.com/shamim-hussain/egt
공식 코드Global Self-Attention as a Replacement for Graph Convolution
https://github.com/shamim-hussain/egt_pytorch
공식 코드Global Self-Attention as a Replacement for Graph Convolution
https://github.com/shamim-hussain/egt_triangular
공식 코드Global Self-Attention as a Replacement for Graph Convolution
https://github.com/shengyu-feng/ariel
공식 코드Adversarial Graph Contrastive Learning with Information Regularization
https://github.com/shenxiaocam/ACDNE
공식 코드Adversarial Deep Network Embedding for Cross-network Node Classification
https://github.com/shenxiaocam/dm_gnn
공식 코드https://github.com/shilinsun/lgcl
공식 코드https://github.com/shwangtangjun/Diff-ResNet
공식 코드Diffusion Mechanism in Residual Neural Network: Theory and Applications
https://github.com/siddhartha047/AGS-GNN
공식 코드AGS-GNN: Attribute-guided Sampling for Graph Neural Networks
https://github.com/sigeisler/s2gnn
공식 코드https://github.com/sisaman/gap
공식 코드GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation
https://github.com/sisaman/lpgnn
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.