Graph Classification
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Graph Classification is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.
<span style="color:grey; opacity: 0.6">( Image credit: Hierarchical Graph Pooling with Structure Learning )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/machadoprx/GATv2-TensorFlow2
https://github.com/mahendrathapa/graph-convolution-network
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/mahequn001/graphlearning/tree/master/model_zoo/gatv2
https://github.com/mahequn001/graphlearning/tree/master/model_zoo/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/mahsa91/GKD
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/mahsa91/RA-GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/manthan2305/Efficient-G-Retriever
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
https://github.com/maplightrx/maplight-tdc
https://github.com/marble0117/GNN_models_pytorch
https://github.com/marble0117/GNN_models_pytorch_geometric
https://github.com/marblet/GNN_models_pytorch_geometric
https://github.com/marblet/gat-pytorch
https://github.com/marionmari/propagation_kernels
https://github.com/masakicktashiro/rgcn_pytorch_implementation
https://github.com/massquantity/LibRecommender
https://github.com/maysambehmanesh/SGCL
https://github.com/meliketoy/graph-cnn.pytorch
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/messi-q/gnnscvuldetector
https://github.com/mfmceneaney/Lambda-GNNs
https://github.com/microsoft/Graphormer
Do Transformers Really Perform Bad for Graph Representation?
https://github.com/microsoft/fs-mol
https://github.com/microsoft/recommenders
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
https://github.com/mikel-brostrom/Deep_Spammer_Detection_GCN
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
https://github.com/mindspore-ai/models/tree/master/official/cv/Alexnet/src
https://github.com/mindspore-ai/models/tree/master/official/gnn/gat
https://github.com/mindspore-ai/models/tree/master/research/gnn/sdne
https://github.com/mindspore-courses/heads-on-mindspore/blob/main/1-best-practice/models/alexnet.py
https://github.com/mitya8128/experiments_notes
https://github.com/mldroid/subgraph2vec
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs
https://github.com/mldroid/subgraph2vec_gensim
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs
https://github.com/mldroid/subgraph2vec_tf
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs
https://github.com/mlzxzhou/keras-gnm
https://github.com/morpheu513/DA_project
HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
https://github.com/morpheu513/NASDAQ_stock_analysis
HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
https://github.com/muhanzhang/DGCNN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.