Search · four archives
Search · four archives
15 papers · ranked by Valyu relevance
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…
Yefeng Fan, Simon Richard White
Exponential random graph models (ERGMs) are flexible probabilistic frameworks to model statistical networks through a variety of network summary statistics. Conventional Bayesian estimation for ERGMs involves iteratively exchanging with an auxiliary variable due to the intractability of the ERGM likelihood. However…
Masami Yoshida
In pedagogical practice, gratitude is recognised not as an emotion, but as an approach to learning. This study introduced gratitude messages into the academic online communication of university students and specifically examined the community in which students shared their messages with gratitude. This study examined…
Qiuchang (Katy) Cao, Holly Dabelko-Schoeny, Keith Warren, Mo Yee Lee
Few studies examined the social network structures within multicultural volunteer programs for low-income diverse older adults, making it unclear how diverse older adults establish social connections beyond their co-ethnic community. This study aims to identify the social network structures within a Senior Companion…
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…
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…
Jingfang Liu, Yafei Liu, Raphael M. Herr
The increasing number of people with anxiety disorders presents challenges when gathering health information. Users in anxiety disorder online communities (ADOCs) share and obtain a variety of health information, such as treatment experience, drug efficacy, and emotional support. This interaction alleviates the…
Yuanyuan Shang, Philip Leifeld
In private capital investment, limited partners (LPs) and general partners (GPs) frequently encounter the challenge of finding suitable counterparts amid limited information, a process often hindered by market inefficiencies. This article addresses this issue by exploring the micro-level mechanisms that shape private…
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…
David Zbíral, Katia Riccardo, Tomáš Hampejs, Zoltán Brys + 1 more
The medieval inquisition of heresy strongly relied on depositions, where witnesses were expected to report on the crimes of others and oneself. The resulting patterns of incrimination could be influenced by various factors, including the characteristics of the underlying dissident social network; the investigators’…
Alfonso Landeros, Dhwani Krishnan, Kenneth Lange, Mary Sehl
Background Many networks contain node and edge data in the form of node-specific covariates and edge weights, respectively. Established methods for investigating graph structure often rely on dichotimizing edge data. This simplification motivates the development of techniques for multivariate analysis. In the context…
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…
Lourens Touwen, Doina Bucur, Remco van der Hofstad, Alessandro Garavaglia + 1 more
'Alessandro Garavaglia' 'Nelly Litvak'] We propose a novel model-selection method for dynamic networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random graph models for dynamic networks, with parameter range chosen…
Selena Wang
The combination of network modeling and psychometric models has opened up exciting directions of research. However, there has been confusion surrounding differences among network models, graphic models, latent variable models and their applications in psychology. In this paper, I attempt to remedy this gap by briefly…