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Found 8 papers, 3 papers with code
Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node Embedding
The proposed GCN-SA contains two enhancements corresponding to edges and node features.
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug Interactions
Within the MVDSC, we utilize multiple DP features to construct graphs, where nodes represent DPs and edges denote different implicit correlations.
Relation-aware graph structure embedding with co-contrastive learning for drug-drug interaction prediction
To alleviate this issue, we propose a novel DDI prediction method based on relation-aware graph structure embedding with co-contrastive learning, RaGSECo.
GCN-SL: Graph Convolutional Network with Structure Learning for Disassortative Graphs
To solve the above-mentioned issue, we propose a graph convolutional network with structure learning (GCN-SL), and furthermore, the proposed approach can be applied to node classification.
GCN-SL: Graph Convolutional Networks with Structure Learning for Graphs under Heterophily
The proposed GCN-SL can aggregate feature representations from nearby nodes via re-connected adjacency matrix and is applied to graphs with various levels of homophily.
R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph
Heterogeneous graph is a kind of data structure widely existing in real life.
Does Normalization Methods Play a Role for Hyperspectral Image Classification?
For Hyperspectral image (HSI) datasets, each class have their salient feature and classifiers classify HSI datasets according to the class's saliency features, however, there will be different salient features when use different normalization method.
Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image
As a new machine learning approach, extreme learning machine (ELM) has received wide attentions due to its good performances.
