We explore the asymptotic properties of strategic models of network formation in very large populations. Specifically, we focus on (undirected) exponential random graph models. We want to recover a ...

This lecture course is devoted to the study of random geometrical objects and structures. Among the most prominent models are random polytopes, random tessellations, particle processes and random ...

In this paper we investigate first passage percolation on an inhomogeneous random graph model introduced by Bollobás et al. (2007). Each vertex in the graph has a type from a type space, and edge ...

Graph machine learning (or graph model), represented by graph neural networks, employs machine learning (especially deep learning) to graph data and is an important research direction in the ...

This paper assesses whether cross-border M&A decisions exhibit network effects. We estimate exponential random graph models (ERGM) and temporal exponential random graph models (TERGM) to evaluate the ...

If open source is the new normal in enterprise software, then that certainly holds for databases, too. In that line of thinking, Github is where it all happens. So to have been favorited 10.000 times ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...