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Jul 25, 2024 · We extract various k-nearest neighbor (kNN) graphs from multiple views of entity features, and then conduct contrastive learning over these kNN graphs.
Jul 25, 2024 · We extract various k-nearest neighbor (kNN) graphs from multiple views of entity features, and then conduct contrastive learning over these kNN graphs.
In this paper, we aim at Graph embedding learning for automatic grasping of low-dimensional node representation on biomedical networks. The purpose is to use ...
May 27, 2024 · We propose MuCoST, a Multi-view graph Contrastive learning framework for deciphering complex Spatially resolved Transcriptomic architectures with dual scale ...
Jul 23, 2024 · Therefore, this paper proposes a Multi-view Mask Contrastive Learning Graph Convolutional Neural Network (MMCL-GCN) for age estimation.
Oct 18, 2023 · We propose a multi-gate mixture of multi-view graph contrastive learning (MMMGCL) method, aiming to get a more reasonable EHR representation and improve the ...
Missing: k- Nearest Neighbor modal
Jul 25, 2024 · Any future updates will be listed below. Multi-view k-Nearest Neighbor Graph Contrastive Learning on Multi-modal Biomedical Data. Crossref DOI ...
Jan 3, 2023 · Graphs are complex data structures which are used to model many complex phenomena in the real world. Graph. Neural Networks (GNN) deal with ...
May 7, 2024 · DiSMVC designs a supervised graph collaborative framework to measure disease similarity. Multiple bio-entity associations related to genes and miRNAs are ...
Missing: modal | Show results with:modal
In these graphs, nodes are particles connected to their k-nearest neighbors. After rounds of message passing, aggregated node representations are used to ...