Abstract: We propose a notion of universality for graph neural networks (GNNs) in the large random graphs limit, tailored for node-level tasks. When graphs are drawn from a latent space model, or from ...

Graph limit theory provides a rigorous framework for analysing sequences of large graphs by representing them as continuous objects known as graphons – symmetric measurable functions on the unit ...

Abstract: Concept drift, characterized by changes in data distribution over time, has always been an inevitable problem in nonstationary data stream environments. Multistream scenarios are ...

This monthly journal, published since 1900, is devoted entirely to research in pure and applied mathematics, and, in general, includes longer papers than those in the Proceedings of the American ...

Since the discovery of quantum mechanics, in the early 20th century, physicists have relied on optics to test its fundamentals. Since the discovery of quantum mechanics, in the early 20th century, ...

We investigate the performance of Dodge's continuous sampling plan 1 (CSP-1) when production run lengths are short or moderate or when the input process is not iid Bernoulli. For finite run lengths, ...