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Search · four archives
21 papers · ranked by Valyu relevance
Alessio Catanzaro, Diego Garlaschelli, Subodh P. Patil
Random graphs offer a useful mathematical representation of a variety of real world complex networks. Exponential random graphs, for example, are particularly suited towards generating random graphs constrained to have specified statistical moments. In this investigation, we elaborate on a generalization of the former…
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…
David Dobáš, Diego Garlaschelli, Petr Jizba
Exponential Random Graphs (ERGs) are among the most widely used network models, derived as principled least-bias graph ensembles that maximize Shannon entropy under constraints on the expected values of given structural properties. However, it has been recently (re)discovered that, in the absence of additional…
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…
Alberto Caimo, Isabella Gollini
Exponential random graph models (ERGMs) are a widely used framework for network data, enabling hypothesis testing on the structural mechanisms underlying observed networks. Bayesian ERGMs provide principled uncertainty quantification and enable the incorporation of prior knowledge through fully probabilistic modelling.…
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…
Zoran Levnajić
Understanding the processes behind the evolution of complex networks is a key objective in network science. An effective framework for tackling this challenge is network model selection, which involves finding the model from a set of candidates that best explains a given network. This book is a systematic review of…
Armand M. Makowski, Siddharth Pal, Geert Verdoolaege
We discuss several limiting degree distributions for a class of homogeneous random graphs, known as random threshold graphs, in the many node regime. This analysis is carried out under a weak assumption on the distribution of the underlying fitness variable. This assumption, which is satisfied by the exponential…
Maxwell H Wang, Till Hoffmann, Jukka‐Pekka Onnela
Mechanistic models can provide an intuitive and interpretable explanation of network growth by specifying a set of generative rules. These rules can be defined by domain knowledge about real-world mechanisms governing network growth or may be designed to facilitate the appearance of certain network motifs. In the…
Pawat Akara-pipattana, Oleg Evnin, Antonio M. Scarfone
The two-star random graph is the simplest exponential random graph model with nontrivial interactions between the graph edges. We propose a set of auxiliary variables that control the thermodynamic limit where the number of vertices N tends to infinity. Such ’master variables’ are usually highly desirable in treatments…
Raima Carol Appaw, Matthew J Silk, Julie Rushmore, Kimberly VanderWaal + 2 more
Detecting patterns in animal social behaviour and movement is complicated by the diversity of ecological, evolutionary, environmental, and biological drivers of these behaviours such as migration, foraging, assortative mixing, socio-ecological factors, and human influence. Addressing these complexities requires a…
A. Y. Klimenko, A. Rozycki, Y. Lu, Zhigang Zheng
We explore a rigorous formulation of agent-based SIR epidemic dynamics as a discrete-state Markov process, capturing the stochastic propagation of infection or an invading agent on networks. Using indicator functions and corresponding marginal probabilities, we derive a hierarchy of evolution equations that resembles…
Roach, Lyndsay, Li, Qiong + 4 more
We propose a covariate-dependent discrete graphical model for capturing dynamic networks among discrete random variables, allowing the dependence structure among vertices to vary with covariates. This discrete dynamic network encompasses the dynamic Ising model as a special case. We formulate a likelihood-based…
Yichao Yao, Minyu Feng, Matjaž Perc, J. Kurths
—Many real-world scale-free networks, such as neural networks and online communication networks, consist of a fixed number of nodes but exhibit dynamic edge fluctuations. However, traditional models frequently overlook scenarios where the node count remains constant, instead prioritizing node growth. In this work, we…
Fernando Alcalde Cuesta, Gustavo Guerberoff, Álvaro Lozano Rojo
In this paper, we study the absorption and fixation times for evolutionary processes on graphs, under different updating rules. While in Moran process a single neighbour is randomly chosen to be replaced, in proliferation processes other neighbours can be replaced using Bernoulli or binomial draws depending on 0 < p ≤…
M. N. Mooij, M. Baudena, A. S. von der Heydt, L. Miele + 1 more
Triangles are abundant in real-world networks but rare in standard null models for sparse graphs. Existing explanations typically rely on explicit triadic closure mechanisms or geometry-based connection rules. We propose an alternative hypothesis: the frequent appearance of triangles may arise naturally from the…
Deniz Sezer, Erdal Toprak
Time-resolved sequencing of pooled mutants is widely used to track their frequencies under selection pressure, thereby revealing variants that are enriched or depleted. Here, we address how to quantify variant growth rates by analyzing the temporal dimension of the counts data through a model of growth. For exponential…
Authors not listed
Accurate prediction of redox potentials of iron (Fe) complexes, in tandem with uncertainty quantification, is essential to advance technologies related to electro-deposition and energy storage by enabling reliable modeling, guiding experimental design, and improving the efficiency of material discovery. Since…
Marco Stock, Florin Ratajczak, Paul Bertin, Eva Hoermanseder + 6 more
Accurate reconstruction of gene regulatory networks (GRNs) from single-cell transcriptomic data remains a major methodological challenge. Recent machine learning approaches, particularly graph neural networks and graph autoencoders, have reported improved performance, yet these gains do not consistently translate to…
Authors not listed
Crystal structure prediction (CSP) is a valuable computational technique used to anticipate the likely crystal structures of a compound of interest. These methods have been proven useful in research and development of pharmaceutical solid forms and in guiding the discovery of materials with targeted properties. Despite…
Luis J Ponce, Esther Li Wen Choo, Diyar Mailepessov, Somya Bansal + 3 more
Estimating the effective reproduction number is crucial for understanding and managing infectious disease outbreaks. For vector-borne diseases like dengue, transmission depends on environmental and spatial conditions: temperature affects the extrinsic incubation period in mosquitoes, altering transmission timing, while…