As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
Penn Engineers have developed a novel design for solar-powered data centers that will orbit Earth and could realistically scale to meet the growing demand for AI computing while reducing the ...
Federated graph learning advances the field of federated learning by enabling privacy-preserving collaborative training on distributed graph data. Conventional federated graph learning methods excel ...
The Connected Data platform provides a Community, Events, and Thought Leadership for those who use the Relationships, Meaning and Context in Data to achieve great things. We've been Connecting Data, ...
Abstract: Self-supervised tasks show significant advantages for node representation learning in recommender systems. This core idea of self-supervised task-based recommender systems depends on data ...
The final, formatted version of the article will be published soon. Background Biomedical knowledge graphs (KGs), such as the Data Distillery Knowledge Graph (DDKG), capture known relationships among ...
Abstract: Empowered by their remarkable advantages, graph neural networks (GNN) serve as potent tools for embedding graph-structured data and finding applications across various domains. Particularly, ...
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