https://github.com/ZeinabRahbar/Implementation-for-the-paper-of-SEMI-SUPERVISED-CLASSIFICATION-WITH-GRAPH-CONVOLUTIONAL-NETWORKS
Semi-Supervised Classification with Graph Convolutional Networks
Papers with Code 아카이브
Node Classification
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- 평가표
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- 보존된 평가 기록
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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/ZhuangCY/Coding-NN
https://github.com/ZhuangCY/DGCN
https://github.com/ZimpleX/gcn-ipdps19
https://github.com/abdcelikkanat/kernelNE
https://github.com/abhilash1910/Deep-Graph-Learning
https://github.com/abhilash1910/GraphAttentionNetworks
https://github.com/abhilash1910/SpectralEmbeddings
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
https://github.com/abi-kothapalli/minimumdominatingsets
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/abojchevski/graph2gauss
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
https://github.com/acbull/pyHGT/tree/master/ogbn-mag
https://github.com/achalagarwal/gcn-lpa
Unifying Graph Convolutional Neural Networks and Label Propagation
https://github.com/agiresearch/InstructGLM
https://github.com/aidos-lab/ECT
Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic Transforms
https://github.com/ajbisberg/gcn
https://github.com/ak24watch/Graph-Deeplearning-GAT
https://github.com/akash18tripathi/MAGNET-Multi-Label-Text-Classi-cation-using-Attention-based-Graph-Neural-Network
https://github.com/alexOarga/haiku-geometric
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/alexOarga/haiku-geometric/blob/main/haiku_geometric/nn/conv/gated_graph_conv.py
https://github.com/alexOarga/haiku-geometric/blob/main/haiku_geometric/nn/conv/gin_conv.py
https://github.com/alexOarga/haiku-geometric/blob/main/haiku_geometric/nn/conv/pna_conv.py
https://github.com/alexfanjn/geomgcn_pyg
https://github.com/alge24/eigenpooling
https://github.com/alililia/ms_contrib/tree/main/ascend_gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/alililia/ms_contrib/tree/main/gpu_gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/allenhaozhu/SSGC
https://github.com/amazon-research/panrep
PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphs
https://github.com/amazon-science/gnn-tail-generalization
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods
https://github.com/amblee0306/Simplicial-Graph-Attention-Network
https://github.com/amin-salehi/GATE
https://github.com/anilakash/indkgc
https://github.com/anish-lu-yihe/SVRT-by-GAT
https://github.com/anon767/CayleyNet
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
https://github.com/anonymous-sourcecode/DifNN
https://github.com/anonymous-sourcecode/GResNet
GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation
https://github.com/anonymous-sourcecode/Graph-Bert
Graph-Bert: Only Attention is Needed for Learning Graph Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.