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Search · four archives
16 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)…
Alex Stivala, Alessandro Lomi
Analysis of the structure of biological networks often uses statistical tests to establish the over-representation of motifs, which are thought to be important building blocks of such networks, related to their biological functions. However, there is disagreement as to the statistical significance of these motifs, and…
Shahadat Uddin, Liaquat Hossain, Jafar Hamra, Ashraful Alam
Background Physician collaboration, which evolves among physicians during the course of providing healthcare services to hospitalised patients, has been seen crucial to effective patient outcomes in healthcare organisations and hospitals. This study aims to explore physician collaborations using measures of social…
Alex Stivala, Garry Robins, Alessandro Lomi, Inés P. Mariño
Exponential random graph models (ERGMs) are widely used for modeling social networks observed at one point in time. However the computational difficulty of ERGM parameter estimation has limited the practical application of this class of models to relatively small networks, up to a few thousand nodes at most, with…
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.…
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…
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…
Saeid Ghafouri, Seyed Hossein Khasteh, Yilun Shang
The uncertainty underlying real-world phenomena has attracted attention toward statistical analysis approaches. In this regard, many problems can be modeled as networks. Thus, the statistical analysis of networked problems has received special attention from many researchers in recent years. Exponential Random Graph…
Helal El-Zaatari, Fei Yu, Michael R. Kosorok, Pablo Martin Rodriguez
This study introduces a novel methodology for endogenous variable selection in Exponential Random Graph Models (ERGMs) to enhance the analysis of social networks across various scientific disciplines. Addressing critical challenges such as ERGM degeneracy and computational complexity, our method integrates a systematic…
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…
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…
John Dell'Italia, Micah A. Johnson, Paul M. Vespa, Martin M. Monti
In recent years, the study of the neural basis of consciousness, particularly in the context of patients recovering from severe brain injury, has greatly benefited from the application of sophisticated network analysis techniques to functional brain data. Yet, current graph theoretic approaches, as employed in the…
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…
Bram Mornie, Didier Colle, Pieter Audenaert, Mario Pickavet + 1 more
'Enrique Hernandez-Lemus'] Testing or benchmarking network algorithms in bioinformatics requires a diverse set of networks with realistic properties. Real networks are often supplemented by randomly generated synthetic ones, but most graph generative models do not take into account the distribution of subgraph…
Evgeny Ivanko, Mikhail Chernoskutov, António M. Lopes
We consider the problem of modeling complex systems where little or nothing is known about the structure of the connections between the elements. In particular, when such systems are to be modeled by graphs, it is unclear what vertex degree distributions these graphs should have. We propose that, instead of attempting…