https://github.com/sayamsingla2000/LightGCN_MovieLens
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
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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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
Molecular Fingerprints Are Strong Models for Peptide Function Prediction
Molecular Fingerprints Are Strong Models for Peptide Function Prediction
Persistence Images: A Stable Vector Representation of Persistent Homology
Semi-Supervised Classification with Graph Convolutional Networks
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
TUDataset: A collection of benchmark datasets for learning with graphs
Semi-Supervised Classification with Graph Convolutional Networks
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
Semi-Supervised Classification with Graph Convolutional Networks
Geometric deep learning on graphs and manifolds using mixture model CNNs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.