Figure 1. Clustering network strategies. (A) Usual graph clustering methods: cluster vertices of a graph into subgraphs. Highly connected vertices form one cluster. (B) Our goal: cluster graphs into ...

The model is a random directed acyclic graph, and its growth is described by a non-Markovian stochastic process. We first prove ergodicity of the stochastic process, and then derive a delay ...

Given these three concerns, we employ the exponential random graph model (ERGM) developed in network science literature (e.g. Snijders 2002, Robins et al. 2007), which can simultaneously account for ...

Donald D. Koban, A Static Bernoulli Random-Graph Model for the Analysis of Covert Networks, Military Operations Research, Vol. 20, No. 4 (2015), pp. 39-47 ...

Incorporating structural features into random-graph calculations should bring theoretical models describing the properties and behaviour of complex networks closer to real-world systems.

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 ...

Visibility Graph (Architecture) In a visibility graph in the context of architecture, nodes represent isovists/viewsheds and edges represent intervisibility of isovists/viewsheds (Turner et al., 2001) ...

In order to specify how firms choose their partners, the so-called exponential random graph model is applied to estimate the ties formation process. For the estimation of such a large-scale network, ...

Derivation of a flexible and analytically tractable block-structured model to reconstruct directed and weighted financial networks, spanning multiple countries, based on the methodologies of fitness ...