The fifty-year quest for universality in percolation theory in high dimensions by T. Ellis, R. Kenna, B. Berche

“…Percolation theory brings added complications such as proliferation of interpenetrating clusters in apparent conflict with suggestions coming from random-graph asymptotics and a dearth of reliable simulational guidance. …”
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The dynamics of inter-firm innovation networks: The case of the photovoltaic industry in China by Tangwei Teng, Xianzhong Cao, Hongting Chen

“…This study employs the Exponential Random Graph Model (ERGM) and Stochastic Actor-Oriented Model (SAOM) to analyse the evolution of the drivers and the formation of the innovation network in China's photovoltaic industry, as a case study. …”
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Statistical Network Analysis with Bergm by Alberto Caimo, Lampros Bouranis, Robert Krause, Nial Friel

“…Recent advances in computational methods for intractable models have made network data increasingly amenable to statistical analysis. Exponential random graph models (ERGMs) emerged as one of the main families of models capable of capturing the complex dependence structure of network data in a wide range of applied contexts. …”
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Physical activity promotion in an urban district: Analyzing the mechanisms of interorganizational cooperation. by Hagen Wäsche, Laura Wolbring, Alexander Woll

“…Social network analysis was applied to examine network properties and exponential random graph models were estimated to test hypotheses concerning mechanisms and conditions of cooperative tie formation. …”
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Maritime Transport Network Analysis: A Critical Review of Analytical Methods and Applications by Maneerat Kanrak, Hong Oanh Nguyen, Yuquan Du

“…Various models such as the random graph model, block model, and ERGM can be used to analyse and explore the formation of a network and interaction between nodes. …”
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Models of similarity in complex networks by Sergey Shvydun

“…The article considers 39 graph distance measures and compares them on simple graphs, random graph models and real networks. The author also evaluates the performance of the models in order to identify which of them can be applied to large networks. …”
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Modelling Strong Control Measures for Epidemic Propagation With Networks—A COVID-19 Case Study by Michael Small, David Cavanagh

“…Our model of city-level transmission of an infectious agent (SEIR model) characterises spread via a (a) scale-free contact network (no control); (b) a random graph (elimination of mass gatherings); and (c) small world lattice (partial to full lockdown-“social” distancing). …”
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God (≡ Elohim), The First Small World Network by Marcel Ausloos, Marcel Ausloos, Marcel Ausloos

“…It is concluded that this graph is a small world network and weakly dis-assortative, because its average local clustering coefficient is significantly higher than a random graph constructed on the same vertex set.…”
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Homophily and Polarization in Twitter Political Networks: A Cross-Country Analysis by Marc Esteve-Del-Valle

“…This research compares the degree of homophily and political polarization in Catalan MPs’ Twitter mentions network to Dutch MPs’ Twitter mentions network. Exponential random graph models were employed on a one-year sample of mentions among Dutch MPs (N = 7,356) and on a one-year, three-month sample of mentions among Catalan MPs (N = 19,507). …”
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The cost of delay in disease response by Tan, Ming Xuan

“…This report details the process of simulating an Infectious Disease Model (SIR) on a random graph network. The SIR parameters used is arbitrary and does not reflect any real life infectious disease such as Covid-19. …”
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Rigid representations of the multiplicative coalescent with linear deletion by Martin, J, Rath, B

“…This process arises for example in connection with a variety of random-graph models which exhibit self-organised criticality. …”

The evolution of subcritical Achlioptas processes by Riordan, O, Warnke, L

“…Although the evolution of such `local' modifications of the Erd{\H o}s--R\'enyi random graph process has received considerable attention during the last decade, so far only rather simple rules are well understood. …”

Counting sets with small sumset and applications by Green, B, Morris, R

“…As a consequence of this and a further new result concerning the number of sets $A \subset \mathbf{Z}/N\mathbf{Z}$ with $|A +A| \leq c |A|^2$, we deduce that the random Cayley graph on $\mathbf{Z}/N\mathbf{Z}$ with edge density~$\frac{1}{2}$ has no clique or independent set of size greater than $\big( 2 + o(1) \big) \log_2 N$, asymptotically the same as for the Erd\H{o}s-R\'enyi random graph. This improves a result of the first author from 2003 in which a bound of $160 \log_2 N$ was obtained. …”

Structure, dynamics, and inference in networks by Chodrow, Philip S.(Philip Samuel)

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Exploring overlapping functional units with various structure in protein interaction networks. by Xiao-Fei Zhang, Dao-Qing Dai, Le Ou-Yang, Meng-Yun Wu

“…Here, we develop a new regularized sparse random graph model (RSRGM) to explore overlapping and various structural functional units in PPI networks. …”
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Simplifying the Process of Going From Cells to Tissues Using Statistical Mechanics by Jagir R. Hussan, Mark L. Trew, Peter J. Hunter

“…A probability distribution of the tissue's network motif can be approximated with exponential random graph models. A prototype problem shows how these approaches could be implemented in practice and the type of information that could be extracted.…”
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Deep Collaborative Learning for Randomly Wired Neural Networks by Ehab Essa, Xianghua Xie

“…In this paper, we created a chain of randomly wired neural networks based on a random graph algorithm and collaboratively trained the models using functional-preserving transfer learning, so that the small network in the chain could learn from the largest one simultaneously. …”
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Sparse block-structured random matrices: universality by Giovanni M Cicuta, Mario Pernici

“…We study ensembles of sparse block-structured random matrices generated from the adjacency matrix of a Erdös–Renyi random graph with N vertices of average degree Z , inserting a real symmetric d  ×  d random block at each non-vanishing entry. …”
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STEM learning communities promote friendships but risk academic segmentation by Wesley Jeffrey, David R. Schaefer, Di Xu, Peter McPartlan, Sabrina Solanki

“…Results of the quasi-experimental analysis indicate that LC participants acquired one additional friend in the major and increased their share of friends in the LC by 54 percentage-points. Exponential random-graph models that test mediation and alternative friendship mechanisms provide support for the theoretical argument that the LC promoted friendship development by structuring opportunities for interaction through block-registration into courses. …”
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Effects of heterogeneous and clustered contact patterns on infectious disease dynamics. by Erik M Volz, Joel C Miller, Alison Galvani, Lauren Ancel Meyers

“…To model epidemic dynamics for this class of random graph, we derive a tractable, low-dimensional system of ordinary differential equations that accounts for the effects of network structure on the course of the epidemic. …”
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