traditional GNNs mostly overlook the relationships between nodes across different graphs, mainly due to their limitation of traditional message passing within each graph. In this paper, we propose a ...

Random graphs and constraint satisfaction problems ... In the realm of CSPs, advancements in message passing algorithms have shown promise for improving the efficiency of solving these problems.

What do you wonder? By The Learning Network A new collection of graphs, maps and charts organized by topic and type from our “What’s Going On in This Graph?” feature. By The Learning ...

The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of ...

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 ...

A web component to represent a graph data structure in a 3-dimensional space using a force-directed iterative layout. Uses ThreeJS/WebGL for 3D rendering and either d3-force-3d or ngraph for the ...

Ever since the introduction of the Google Knowledge Graph, a growing number of organizations have adopted this powerful technology to drive efficiency and effectiveness in their data management.

However, they have shortcomings in extracting meaningful spatial and temporal relationships. We propose a supervised data-driven method to predict S-wave velocity using a graph convolutional network ...