Researchers have developed a new tool, bimodularity, that adds directionality to community detection in networks.

As summer winds down, many of us in continental Europe are heading back north. The long return journeys from the beaches of ...

Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks.

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful.

We want to devote this issue to sharing and celebrating the stories of collective efforts that make digital inclusion ...

Graph neural networks are very powerful tools. They have already found powerful applications in domains such as route planning, fraud detection, network optimization, and drug research.

Graph networks are designed to promote building complex architectures using customizable graph-to-graph building blocks, and their relational inductive biases promote combinatorial generalization and ...

To overcome such inherent challenges with graph neural networks and improve recommendation abilities, LinkedIn has created a process it calls Performance-Adaptive Sampling Strategy (PASS). that ...