The Based DEX has unveiled the tokenomics for its upcoming native token launch, and about 60% will go to the community and ...
Methods: We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of ...
Graph neural networks (GNN) rely on graph operations that include neural network training for various graph related tasks. Recently, several attempts have been made to apply the GNNs to functional ...
Integrate the Microsoft Graph API into your .NET project! The Microsoft Graph .NET Core Client Library contains core classes and interfaces used by Microsoft.Graph Client Library to send native HTTP ...
GraphStorm is an enterprise-grade graph machine learning (GML) framework designed for scalability and ease of use. It simplifies the development and deployment of GML models on industry-scale graphs ...
Abstract: Lipschitz extensions were proposed as a tool for designing differentially private algorithms for approximating graph statistics. However, efficiently computable Lipschitz extensions were ...
The Boundless team, developing the zero-knowledge proof network and marketplace launched by RISC Zero, has found a way to use Bitcoin as a settlement and verification layer for computationally ...
Abstract: The explosive growth of mobile data traffic has prompted operators to deploy a large number of base stations (BSs). However, due to the uneven traffic distribution, many BSs remain ...
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