https://github.com/liun-online/cogsl
공식 코드Compact Graph Structure Learning via Mutual Information Compression
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/liun-online/lsir
공식 코드https://github.com/liyuechao1998/scgsl
공식 코드https://github.com/ljs-student/grafn
공식 코드https://github.com/ljxw88/deephgcn
공식 코드DeepHGCN: Toward Deeper Hyperbolic Graph Convolutional Networks
https://github.com/llan-ml/MetaTNE
공식 코드Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding
https://github.com/llqy123/LLGC-master
공식 코드L^2GC:Lorentzian Linear Graph Convolutional Networks for Node Classification
https://github.com/lmu-dbs/diffusal
공식 코드DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification
https://github.com/lorenzodallamico/NishimoriBetheHessian
공식 코드Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphs
https://github.com/louzounlab/graph-al
공식 코드https://github.com/lrjconan/LanczosNetwork
공식 코드https://github.com/lspongebobjh/virgo_icml2023
공식 코드https://github.com/lucfra/LDS
공식 코드https://github.com/lucfra/LDS-GNN
공식 코드https://github.com/lucianoavinas/lowrank-gnn-kernels
공식 코드https://github.com/lukangkang123/ec-gnn
공식 코드Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction
https://github.com/lukecavabarrett/pna
공식 코드https://github.com/lumia-group/graphparsingnetworks
공식 코드https://github.com/lumia-group/orderedgnn
공식 코드Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing
https://github.com/lumia-group/subtree-attention
공식 코드https://github.com/luoyk1999/hdse
공식 코드Enhancing Graph Transformers with Hierarchical Distance Structural Encoding
https://github.com/lyvykhang/edgehetero-nodeproppred
공식 코드Article Classification with Graph Neural Networks and Multigraphs
https://github.com/m-lampert/self-loop-paradox
공식 코드The Self-Loop Paradox: Investigating the Impact of Self-Loops on Graph Neural Networks
https://github.com/mahalakshmi-sabanayagam/NTK_GCN
공식 코드https://github.com/mahsa91/RA-GCN
공식 코드RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data
https://github.com/mahsa91/py_mgcn
공식 코드MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks
https://github.com/mala-lab/augcl
공식 코드Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning
https://github.com/mala-lab/tpp
공식 코드Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach
https://github.com/malllabiisc/ASAP
공식 코드ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
https://github.com/malllabiisc/CompGCN
공식 코드Composition-based Multi-Relational Graph Convolutional Networks
https://github.com/manhtuando97/two-stage-gnn
공식 코드Two-stage Training of Graph Neural Networks for Graph Classification
https://github.com/manoskary/musgconv
공식 코드Perception-Inspired Graph Convolution for Music Understanding Tasks
https://github.com/manvic14/survey_fairness_graphs
공식 코드https://github.com/marblet/GCNmf
공식 코드Graph Convolutional Networks for Graphs Containing Missing Features
https://github.com/marlin-codes/kHGCN
공식 코드kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning
https://github.com/mashaan14/PAtree-SGC
공식 코드Graph Construction using Principal Axis Trees for Simple Graph Convolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.