Analysing Local Sparseness in the Macaque Brain Network. by Raghavendra Singh, Seema Nagar, Amit A Nanavati

“…We propose a novel metric to measure the topological communication load on a vertex due to its immediate neighbourhood, and show that in terms of distribution of this local communication load, a network of Macaque long distance pathways is substantially different from other real world networks and random graph models. Macaque network contains the entire range of local subnetworks, from star-like networks to clique-like networks, while other networks tend to contain a relatively small range of subnetworks. …”
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Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities. by Darko Cherepnalkoski, Andreas Karpf, Igor Mozetič, Miha Grčar

“…The first one is based on Krippendorff's Alpha reliability, used to measure the agreement between raters in data-analysis scenarios, and the second one is based on Exponential Random Graph Models, often used in social-network analysis. …”
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Graph Theory for Modeling and Analysis of the Human Lymphatic System by Rostislav Savinkov, Dmitry Grebennikov, Darya Puchkova, Valery Chereshnev, Igor Sazonov, Gennady Bocharov

“…A computational algorithm for the generation of the rule-based random graph is developed and implemented. Some fundamental characteristics of the two types of HLS graph models are analyzed using different metrics such as graph energy, clustering, robustness, etc.…”
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Information Network Among Farmers: A Case Study in Ghana by Qian Yu, Patience Pokuaa Gambrah

“…The data was then analyzed using descriptive statistics and an exponential random graph model (ERGM). The results of this study showed that farmers’ information networks had a positive effect on their performance and productivity. …”
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Resilience Characteristics and Driving Mechanism of Urban Collaborative Innovation Network—A Case Study of China’s New Energy Vehicle Industry by Yuyue Guan, Longfei Li, Chao Liu

“…Through our analysis of the network’s resilience characteristics and evolution, we investigate the driving mechanisms behind its formation using the exponential random graph model (ERGM). Empirical results demonstrate that the urban collaborative innovation network is expanding and strengthening, with increased resilience and the ability to withstand uncertainty. …”
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A Useful Criterion on Studying Consistent Estimation in Community Detection by Huan Qing

“…We summarize the idea of using a separation condition for a standard network and sharp threshold of the Erdös–Rényi random graph to study consistent estimation, and compare theoretical error rates and requirements on the network sparsity of spectral methods under models that can degenerate to a stochastic block model as a four-step criterion SCSTC. …”
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Dyads, triads, and tetrads: a multivariate simulation approach to uncovering network motifs in social graphs by Diane Felmlee, Cassie McMillan, Roger Whitaker

“…Given that the correct control distribution for detecting motifs is a matter of continuous debate, we propose a novel approach that compares the local patterns of observed networks to random graphs simulated from exponential random graph models. …”
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Asymptotic properties of some minor-closed classes of graphs (conference version) by Mireille Bousquet-Mélou, Kerstin Weller

“…Let $\mathcal{A}$ be a minor-closed class of labelled graphs, and let $G_n$ be a random graph sampled uniformly from the set of n-vertex graphs of $\mathcal{A}$. …”
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Social connectivity and adaptive capacity strategies in large-scale fisheries by Iratxe Rubio, Jacob Hileman, Elena Ojea

“…We use cluster analysis, descriptive statistics, and exponential random graph models to assess whether different types of actors, occupying different network positions, value similar adaptive capacity strategies. …”
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Split4Blank: Maintaining consistency while improving efficiency of loading RDF data with blank nodes. by Atsuko Yamaguchi, Yasunori Yamamoto

“…In addition, we generated synthetic RDF datasets to evaluate scalability based on the properties of various graphs, such as a scale-free and random graph.…”
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Frequency response and gap tuning for nonlinear electrical oscillator networks. by Harish S Bhat, Garnet J Vaz

“…Running numerical experiments using three different random graph models, we show that shrinking the gap between the graph Laplacian's first two eigenvalues dramatically improves a network's ability to (i) transfer energy to higher harmonics, and (ii) generate large-amplitude signals. …”
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Structure and evolution of communication networks in organizations by Qian, Jichao, M. Eng. Massachusetts Institute of Technology

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Wireless secrecy in large-scale networks by Pinto, Pedro C., Barros, Joao, Win, Moe Z.

“…The intrinsically secure communications graph (iS-graph) is a random graph which describes the connections that can be securely established over a large-scale network, by exploiting the physical properties of the wireless medium. …”
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Understanding interactions in virtual HIV communities: a social network analysis approach by Shi, Jingyuan, Wang, Xiaohui, Peng, Tai-Quan, Chen, Liang

“…Specifically, we explained the probability of forming interaction ties with homophily and popularity characteristics. The exponential random graph modeling results showed that members in this community tend to form homophilous ties in terms of shared location and interests. …”
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Slow emergence of the giant component in the growing m-out graph by Bollobas, B, Riordan, O

“…Let H m(n) be a random graph on n vertices, grown by adding vertices one at a time, joining each new vertex to a uniformly chosen set of m earlier vertices. …”

Fast sampling via spectral independence beyond bounded-degree graphs by Bezáková, I, Galanis, A, Goldberg, LA, Štefankovič, D

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

Stakeholder engagement variability across public, private and public-private partnership projects: A data-driven network-based analysis by Shahadat Uddin, Stephen Ong, Petr Matous

“…To further interpret the data, exponential random graph models were also applied to determine the most statistically prevalent network motifs within each type of project. …”
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Social network analysis of multi-stakeholder platforms in agricultural research for development: Opportunities and constraints for innovation and scaling. by Frans Hermans, Murat Sartas, Boudy van Schagen, Piet van Asten, Marc Schut

“…In order to do this, we apply Social Network Analysis and Exponential Random Graph Modelling (ERGM) to investigate the structural properties of the collaborative, knowledge exchange and influence networks of these MSPs and compared them against value propositions derived from the innovation network literature. …”
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Comparing brain networks of different size and connectivity density using graph theory. by Bernadette C M van Wijk, Cornelis J Stam, Andreas Daffertshofer

“…We also add a number of methods used in social sciences that build on statistics of local network structures including exponential random graph models and motif counting. We show that none of the here-investigated methods allows for a reliable and fully unbiased comparison, but some perform better than others.…”
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Solving hard computational problems efficiently: asymptotic parametric complexity 3-coloring algorithm. by José Antonio Martín H

“…The algorithm was tested on random graphs, planar graphs and 4-regular planar graphs. …”
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