The ordinary graphite in pencil lead is proving to be surprisingly multifaceted at the microscale. In a study published in ...

Decades ago, Paul Erdős used randomness to illuminate the vast and weird world of networks. Now mathematicians are making his ...

Abstract: Spectral clustering (SC) has been applied to analyze varieties of data structures over the past few decades owing to its outstanding breakthrough in graph learning. However, the ...

Finding and developing new molecules is one of the great research endeavours of modern chemistry. From the development of new drugs to the creation of more sustainable materials, everything depends on ...

Accurately identifying small molecule binding sites on proteins is fundamental to understanding protein function and enabling structure-based drug discovery, yet this critical step remains a major ...

Figure 1. StructureNet is a hierarchical graph network that produces a unified latent space to encode structured models with both continuous geometric and discrete structural variations. In this ...

Google published details of a new kind of AI based on graphs called a Graph Foundation Model (GFM) that generalizes to previously unseen graphs and delivers a three to forty times boost in precision ...

Many biological networks are modeled with multivariate discrete dynamical systems. Current theory suggests that the network of interactions captures salient features of system dynamics, but it misses ...

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 ...