Fast maximum likelihood estimation via equilibrium expectation for large network data

This is the final version. Available from the publisher via the DOI in this record.A major line of contemporary research on complex networks is based on the development of statistical models that specify the local motifs associated with macro-structural properties observed in actual networks. This statistical approach becomes increasingly problematic as network size increases. In the context of current research on efficient estimation of models for large network data sets, we propose a fast algorithm for maximum likelihood estimation (MLE) that affords a significant increase in the size of networks amenable to direct empirical analysis. The algorithm we propose in this paper relies on properties of Markov chains at equilibrium, and for this reason it is called equilibrium expectation (EE). We demonstrate the performance of the EE algorithm in the context of exponential random graph models (ERGMs) a family of statistical models commonly used in empirical research based on network data observed at a single period in time. Thus far, the lack of efficient computational strategies has limited the empirical scope of ERGMs to relatively small networks with a few thousand nodes. The approach we propose allows a dramatic increase in the size of networks that may be analyzed using ERGMs. This is illustrated in an analysis of several biological networks and one social network with 104,103 nodes.Swiss National Science Foundatio

Simulation of synthesis of cluster-assembled nanostructured materials

A model is described to simulate the formation of nanostructured materials by cluster beam deposition. Clusters are modelled by spherical balls with a given size distribution function, which fall to the substrate and stick to the growing structure. The mobility of clusters along the film surface is modelled by introduction of a critical angle at which a falling ball meets a ball that belongs to the structure. When the falling ball touches one of the balls in the structure at an angle smaller than the critical one, it sticks to the film; otherwise the ball rolls along the surface till it meets other balls. It is shown that a variety of structures similar to those observed experimentally can be produced. The density of the model structures, percolation thresholds and the surface roughness are analyzed.Описано модель утворення наноструктурних матеріалів при осадженні кластерних потоків. Кластери сферичної форми з заданою функцією розподілу за розмірами падають і прилипають до структури, що росте на підкладці. Ймовірність прилипання контролюється критичним кутом скочування по поверхні структури. Показано, що можна отримати різноманітні структури подібні до тих, що спостерігаються експериментально. Щільність структур, пороги протікання, поверхнева шорсткість досліджуються в залежності від параметрів моделі

Simulation of Self-heating Process on the Nanoscale: a Multiscale Approach for Molecular Models of Nanocomposite Materials

We develop a theoretical–computational protocol to simulate Joule heating on nanocomposite materials, allowing to investigate morphology, voltage and concentration effects

Hybrid Particle-Field Molecular Dynamics Under Constant Pressure

Hybrid particle-field methods are computationally efficient approaches for modelling soft matter systems. So far applications of these methodologies have been limited to constant volume conditions. Here, we reformulate particle-field interactions to represent systems coupled to constant external pressure. First, we show that the commonly used particle-field energy functional can be modified to model and parameterize the isotropic contributions to the pressure tensor without interfering with the microscopic forces on the particles. Second, we employ a square gradient particle-field interaction term to model non-isotropic contributions to the pressure tensor, such as in surface tension phenomena. This formulation is implemented within the hybrid particle-field molecular dynamics approach and is tested on a series of model systems. Simulations of a homogeneous water box demonstrate that it is possible to parameterize the equation of state to reproduce any target density for a given external pressure. Moreover, the same parameterization is transferable to systems of similar coarse-grained mapping resolution. Finally, we evaluate the feasibility of the proposed approach on coarse-grained models of phospholipids, finding that the term between water and the lipid hydrocarbon tails is alone sufficient to reproduce the experimental area per lipid in constant-pressure simulations, and to produce a qualitatively correct lateral pressure profile.Comment: 24 pages, 7 figure

Bayesian exponential random graph modelling of interhospital patient referral networks

Using original data that we have collected on referral relations between 110 hospitals serving a large regional community, we show how recently derived Bayesian exponential random graph models may be adopted to illuminate core empirical issues in research on relational coordination among health care organisations. We show how a rigorous Bayesian computation approach supports a fully probabilistic analytical framework that alleviates well-known problems in the estimation of model parameters of exponential random graph models. We also show how the main structural features of interhospital patient referral networks that prior studies have described, can be reproduced with accuracy by specifying the system of local dependencies that produce – but at the same time are induced by – decentralised collaborative arrangements between hospitals

Phase transformations and segregation in Fe-Ni alloys and nanoalloys

Ordering and segregation properties of Fe-Ni alloys and nanoalloys are investigated by means of Metropolis Monte Carlo (MMC) and molecular dynamics (MD) simulations. The model is based on an embedded atom potential which, according to thermodynamic integration, only stabilizes those phases that are observed experimentally. This stability is confirmed by MMC and the same phases are found stable in truncated octahedral nanoparticles containing no more than 201 atoms. At given composition, Ni segregates at {100} and nanoparticle surfaces on the Fe-rich side of the phase diagram, Fe segregates at intermediate compositions and no significant trend is predicted on the Ni-rich side. A BCC to L10 transition is observed to occur at a Ni fraction close to 0.32, both in bulk alloys and in nanoparticles. The transition gives rise to a change in the nanoparticle aspect ratio by a factor 2 1/2. Using MD, by varying temperature, it was possible to monitor a BCC to FCC transition in solid solution nanoparticles reversibly. © Springer Science+Business Media, LLC 2012.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

The effect of encapsulation in carbon nanotubes on properties of Fe-Ni nanoalloys with cubic and helical structures

The effect of encapsulation in carbon nanotubes on Fe-Ni nanoparticles (NPs), NP assemblies and nanowires (NWs) is studied by means of atomistic simulations with empirical potentials. The BCC Fe, L10, FeNi, L1 2 FeNi3 and FCC Ni stable phases of the bulk alloy are retrieved for both the freestanding (FS) and the encapsulated nanoalloys. As it requires large morphological changes, the BCC/L10 transition may be inhibited by the confinement in a nanotube. The results indicate that encapsulation has the effect to enhance Fe segregation at compositions intermediate between stable phases. When the nanotube is too narrow, encapsulated NWs do not support a cubic structure. They consist in coaxial layers with a central straight atomic row aligning with the tube axis. Each layer displays a helical structure which can be equivalently viewed as a folded atomic plane with low Miller indices. Such ultrathin helical Fe-Ni NWs, FS as well as encapsulated, behave as almost ideal solid solutions over the whole range of compositions. © 2012 Springer Science+Business Media, LLC.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Patent citation network analysis: A perspective from descriptive statistics and ERGMs.

Patent Citation Analysis has been gaining considerable traction over the past few decades. In this paper, we collect extensive information on patents and citations and provide a perspective of citation network analysis of patents from a statistical viewpoint. We identify and analyze the most cited patents, the most innovative and the highly cited companies along with the structural properties of the network by providing in-depth descriptive analysis. Furthermore, we employ Exponential Random Graph Models (ERGMs) to analyze the citation networks. ERGMs enables understanding the social perspectives of a patent citation network which has not been studied earlier. We demonstrate that social properties such as homophily (the inclination to cite patents from the same country or in the same language) and transitivity (the inclination to cite references' references) together with the technicalities of the patents (e.g., language, categories), has a significant effect on citations. We also provide an in-depth analysis of citations for sectors in patents and how it is affected by the size of the same. Overall, our paper delves into European patents with the aim of providing new insights and serves as an account for fitting ERGMs on large networks and analyzing them. ERGMs help us model network mechanisms directly, instead of acting as a proxy for unspecified dependence and relationships among the observations