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Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different ...

GCN, a groundbreaking disentangled graph convolutional network that dynamically adjusts feature channels for enhanced node ...

The researchers validate SySTeC’s effectiveness through extensive performance evaluations on common tensor operations, including symmetric sparse matrix-vector multiplication (SSYMV), ...

Abstract: Optimal Transport (OT) theory has seen increasing attention from the computer science community due to its potency and relevance in modeling ... To this end, we cover the following: computer ...

According to the market assumptions, GRT is set to trade higher in years to come. However, factors like market crashes or difficult regulations could invalidate this bullish theory. The Graph ...

Graph neural network (GNN) architectures have emerged as promising force field models, exhibiting high accuracy in predicting complex energies and forces based on atomic identities and Cartesian ...

We are a research-led school with a reputation for excellent teaching and courses with real-world impact. We are based across four sites, comprising more than 2000 students and 190 specialist staff ...

Today, it might be turning your computer off and on again ... the most precisely tested predictions in all of science, with agreement between theory and experiment stretching to many decimal ...

Computer Science is a rich and dynamic discipline that seeks to understand and use effectively the great potential of computing. Computers and algorithmic problem solving have revolutionized the 20th ...

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 ...