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Search · four archives
24 papers · ranked by Valyu relevance
Bernadette C. M. van Wijk, Cornelis J. Stam, Andreas Daffertshofer, Olaf Sporns
'Olaf Sporns'] Graph theory is a valuable framework to study the organization of functional and anatomical connections in the brain. Its use for comparing network topologies, however, is not without difficulties. Graph measures may be influenced by the number of nodes (N) and the average degree (k) of the network. The…
Sean L. Simpson, Satoru Hayasaka, Paul J. Laurienti, Olaf Sporns
Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks)…
David A. Rolls, Peng Wang, Emma McBryde, Philippa Pattison + 2 more
We compare two broad types of empirically grounded random network models in terms of their abilities to capture both network features and simulated Susceptible-Infected-Recovered (SIR) epidemic dynamics. The types of network models are exponential random graph models (ERGMs) and extensions of the configuration model.…
Alberto Caimo, Nial Friel
Exponential random graph models are a class of widely used exponential family models for social networks. The topological structure of an observed network is modelled by the relative prevalence of a set of local sub-graph configurations termed network statistics. One of the key tasks in the application of these models…
Can Jiao, Ting Wang, Jianxin Liu, Huanjie Wu + 2 more
'Xiaozhe Peng'] The influences of peer relationships on adolescent subjective well-being were investigated within the framework of social network analysis, using exponential random graph models as a methodological tool. The participants in the study were 1,279 students (678 boys and 601 girls) from nine junior middle…
B.C.L. Lehmann, R.N. Henson, L. Geerligs, S.R. White
The brain can be modelled as a network with nodes and edges derived from a range of imaging modalities: the nodes correspond to spatially distinct regions and the edges to the interactions between them. Whole-brain connectivity studies typically seek to determine how network properties change with a given categorical…
Viviana Amati, Angus Mol, Termeh Shafie, Corinne Hofman + 1 more
'Ulrik Brandes'] Reconstructing ties between archaeological contexts may contribute to explain and describe a variety of past social phenomena. Several models have been formulated to infer the structure of such archaeological networks. The applicability of these models in diverse archaeological contexts is limited by…
Johann Mourier, Marc Soria, Matthew Silk, Angélique Demichelis + 2 more
Animal movements are typically influenced by multiple environmental factors simultaneously and individuals vary in their response to this environmental heterogeneity. Therefore, understanding how environmental aspects, including biotic, abiotic and anthropogenic factors, influence the movements of wild animals is an…
Denise Helena Silva Duarte, Rafael Honório Pereira Alves
A class of models that have been widely used are the exponential random graph (ERG) models, which form a comprehensive family of models that include independent and dyadic edge models, Markov random graphs, and many other graph distributions, in addition to allow the inclusion of covariates that can lead to a better…
Brieuc Lehmann, Simon White
The collection of data on populations of networks is becoming increasingly common, where each data point can be seen as a realisation of a network-valued random variable. Moreover, each data point may be accompanied by some additional covariate information and one may be interested in assessing the effect of these…
Ming Cao
As a representation of relational data over time series, longitudinal networks provide opportunities to study link formation processes. However, networks at scale often exhibits community structure (i.e. clustering), which may confound local structural effects if it is not considered appropriately in statistical…
Mei Yin
The exponential family of random graphs is among the most widelystudied network models. We show that any exponential random graph model may alternatively be viewed as a lattice gas model with a finite Banach space norm. The system may then be treated by cluster expansion methods from statistical mechanics. In…
Alberto Caimo, Nial Friel
Networks are relational data that can be defined as a collection of nodes interacting with each other and connected in a pairwise fashion. From a statistical point of view, networks are relational data represented as mathematical graphs. A graph consists of a set of n nodes and a set of m edges which define some sort…
Fan Yin, Carter T. Butts, Fabrizio De Vico Fallani
The exponential family random graph modeling (ERGM) framework provides a highly flexible approach for the statistical analysis of networks (i.e., graphs). As ERGMs with dyadic dependence involve normalizing factors that are extremely costly to compute, practical strategies for ERGMs inference generally employ a variety…
Samuel M. Jenness, Steven M. Goodreau, Martina Morris
EpiModel provides tools for building, simulating, and analyzing mathematical models for the population dynamics of infectious disease transmission in R. Several classes of models are included, but the unique contribution of this software package is a general stochastic framework for modeling the spread of epidemics on…
Madhurima Nath, Yihui Ren, Yasamin Khorramzadeh, Stephen Eubank
We demonstrate a general method to analyze the sensitivity of attack rate in a network model of infectious disease epidemiology to the structure of the network. We use Moore and Shannon’s “network reliability” statistic to measure the epidemic potential of a network. A number of networks are generated using exponential…
Arend Hintze, Christoph Adami
Background Much work in systems biology, but also in the analysis of social network and communication and transport infrastructure, involves an in-depth analysis of local and global properties of those networks, and how these properties relate to the function of the network within the integrated system. Most often…
Pratha Sah, Lisa O. Singh, Aaron Clauset, Shweta Bansal
A modular pattern, also called community structure, is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a system’s functional components and the impact of local structures on dynamics at a global…
Antonino Freno, Mikaela Keller, Gemma C. Garriga, Marc Tommasi
Generative models for graphs have been typically committed to strong prior assumptions concerning the form of the modeled distributions. Moreover, the vast majority of currently available models are either only suitable for characterizing some particular network properties (such as degree distribution or clustering…
Bertrand Ottino-Löffler, Jacob G. Scott, Steven H. Strogatz
We study a stochastic model of infection spreading on a network. At each time step a node is chosen at random, along with one of its neighbors. If the node is infected and the neighbor is susceptible, the neighbor becomes infected. How many time steps T does it take to completely infect a network of N nodes, starting…
Juho Lee, Creighton Heaukulani, Zoubin Ghahramani, Lancelot F. James + 1 more
'Seungjin Choi'] We present a model for random simple graphs with power law (i.e., heavy-tailed) degree distributions. To attain this behavior, the edge probabilities in the graph are constructed from Bertoin–Fujita–Roynette–Yor (BFRY) random variables, which have been recently utilized in Bayesian statistics for the…
Laura Eslava, Sayle Sigarreta Ricardo, Arno Siri-Jégousse
We prove that the generalized Randić index over graphs following the Erdos-Rényi model, for both the sparse and dense regimes, is concentrated around its mean when the number of vertices tends to infinity.
Charles Eads
This report describes and illustrates a set of automatable multicomponent exponential relaxation analysis protocols that are model-agnostic and suited to extracting information under circumstances when little prior knowledge about the underlying system is used. Methods are illustrated and mathematical and physical…
harry gray
The matrix exponential method as implemented in MATLAB is demonstrated as a facile tool for solving the time-dependent concentrations of an arbitrary chemically reactive network modelled as a coupled linear system of first-order differential equations. The method is used to verify a 10 species network incorporating…