Imagine standing atop a mountain, gazing at the vast landscape below, trying to make sense of the world around you. For centuries, explorers relied on such vantage points to map their surroundings.

Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different neighbors when aggregating their features to update a ...

One theme cuts across the most credible 2026 predictions: autonomy will be driven less by bigger models and more by better ...

Giulia Livieri sets out remarkable new research with results that clarify how learning works on complex graphs and how quickly any method (including Graph Convolutional Networks) can learn from them, ...

In algorithms, as in life, negativity can be a drag. Consider the problem of finding the shortest path between two points on a graph — a network of nodes connected by links, or edges. Often, these ...