Modeling the live-pig trade network in Georgia: Implications for disease prevention and control. by Esther Andrea Kukielka, Beatriz Martínez-López, Daniel Beltrán-Alcrudo

“…Social network analysis and exponential random graph models were used to better understand the structure, contact patterns and main drivers for pig trade in the country. …”
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Analysing the Structure of the Global Wheat Trade Network: An ERGM Approach by Ester Gutiérrez-Moya, Sebastián Lozano, Belarmino Adenso-Díaz

“…There are some differences, however, in the magnitude of some measures (e.g., connectivity or disassortativity), and a higher degree of inequality in the distribution of the number of partners and the distribution of trade volume in the period 2014–2018. An Exponential Random Graph Model (ERGM) has been applied to identify significant determinants associated with the presence/absence of trade links between countries. …”
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Prestige and homophily predict network structure for social learning of medicinal plant knowledge. by Matthew O Bond, Orou G Gaoue

“…We developed exponential random graph models (ERGMs) to test whether hypothesized patterns of knowledge sharing based on prestige and homophily are more common in the observed network than in randomly simulated networks of the same size. …”
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Engagement in water governance action situations in the Lake Champlain Basin. by Patrick Bitterman, Christopher Koliba

“…We apply exponential random graph models to quantify the effects of scale, issues, and homophily on actor participation in these forums. …”
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BETWEEN THE RIGHT AND THE COMMON. HOW GROUPS REACT TO SOCIALLY UNDESIRABLE BEHAVIOUR by Komendant-Brodowska Agata

“…Computer simulations were conducted for networks of a specific type (Erd¨os-R´enyi random graph). The main aim of the analysis was to identify non-structural and structural features of the group that can impede or even block the intervention of the group. …”
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Understanding interaction network formation across instructional contexts in remote physics courses by Meagan Sundstrom, Andy Schang, Ashley B. Heim, N. G. Holmes

“…We apply statistical methods from social network analysis—exponential random graph models—to measure the relationship between network formation and multiple variables: students’ discussion and lab section enrollment, final course grades, gender, and race or ethnicity. …”
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The large graph limit of a stochastic epidemic model on a dynamic multilayer network by Karly A. Jacobsen, Mark G. Burch, Joseph H. Tien, Grzegorz A. Rempała

“…The network is given by a random graph following a multilayer configuration model where edges in different layers correspond to potentially infectious contacts of different types. …”
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Strategic robustness in bi-level system-of-systems design by Jordan L. Stern, Ambrosio Valencia-Romero, Paul T. Grogan

“…Models are constructed on small world, preferential attachment and random graph topologies and executed in batch simulations. …”
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Personality traits, self-efficacy, and friendship establishment: Group characteristics and network clustering of college students’ friendships by Dongdong Yan, Xi Yang, Huanzhe Zhang

“…In this study, we used social network analysis method and exponential random graph model (ERGM). The following findings are reported. …”
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TRANSFERENCE FOR THE ERDŐS–KO–RADO THEOREM by JÓZSEF BALOGH, BÉLA BOLLOBÁS, BHARGAV P. NARAYANAN

“…Delete the edges of $K(n,r)$ with some probability, independently of each other: is the independence number of this random graph equal to the independence number of the Kneser graph itself? …”
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Learning and reasoning with graph data by Manfred Jaeger

“…Based on this semantic foundation we introduce a taxonomy of reasoning tasks that casts problems ranging from transductive link prediction to asymptotic analysis of random graph models as queries of different complexities for a given model. …”
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Solidity in a Turbulent Flow by Wenyi Shang, Zizhou Sang

“…This article uses network simulation and Exponential Random Graph Models (ERGM) to describe the structure of this network. …”
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SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms by Verschoren Alain, Ma Hongwu, van Remortel Piet, Naudts Bart, Van Leemput Koenraad, Van den Bulcke Tim, De Moor Bart, Marchal Kathleen

“…Our results show that the statistical properties of these topologies more closely approximate those of genuine biological networks than do those of different types of random graph models. Several user-definable parameters adjust the complexity of the resulting data set with respect to the structure learning algorithms.…”
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A Nearly Tight Sum-of-Squares Lower Bound for the Planted Clique Problem by Barak, Boaz, Hopkins, Samuel, Kelner, Jonathan, Kothari, Pravesh K, Moitra, Ankur, Potechin, Aaron

“…© 2019 Society for Industrial and Applied Mathematics We prove that with high probability over the choice of a random graph G from the Erd\H os-Rényi distribution G(n, 1/2), the nO(d)-time degree d sum-of-squares (SOS) semidefinite programming relaxation for the clique problem will give a value of at least n1/2 - c(d/ log n)1/2 for some constant c > 0. …”
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Structural Analysis of Laplacian Spectral Properties of Large-Scale Networks by Preciado, Victor M., Jadbabaie, Ali, Verghese, George C.

“…As a consequence, random graph models in which only local structural features are prescribed are, in general, inadequate to faithfully model Laplacian spectral properties of a network.…”
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Fast sampling via spectral independence beyond bounded-degree graphs by Bezakova, I, Galanis, A, Goldberg, L, Stefankovic, D

“…As a main application of our techniques, we consider the random graph G(n,d/n), where the previously known algorithms run in time n^O(log d) or applied only to large d. …”

Degree distribution of the FKP network model. by Berger, N, Bollobás, B, Borgs, C, Chayes, J, Riordan, O

“…Recently, these observations have triggered much work attempting to explain the power laws in terms of new 'scale-free' random graph models. So far, perhaps the most effective mechanism for explaining power laws is the combination of growth and preferential attachment. …”

The scaling limit of a critical random directed graph by Goldschmidt, C, Stephenson, R

“…Our proofs rely on a depth-first exploration of the graph which enables us to relate the strongly connected components to a particular spanning forest of the undirected Erd˝os–Rényi random graph G(n,p), whose scaling limit is well understood. …”

Trees and graphs: congestion, polynomials and reconstruction by Law, H

“…In particular, by partitioning a hypercube into pieces with almost optimal edge-boundaries, we give tight estimates of the parameter thereby disproving a conjecture of Hruska (2008). For a typical random graph, the parameter exhibits a zigzag behaviour reflecting the feature that it is not monotone in the number of edges. …”

Alignment-free protein interaction network comparison. by Ali, W, Rito, T, Reinert, G, Sun, F, Deane, C

“…RESULTS: We first demonstrate that Netdis is able to correctly separate different random graph model types independent of network size and density. …”