arXiv subjects

Göran Kauermann

Publications and source records attributed to Göran Kauermann.

49 records · Page 3Linked to original sources

arXiv2020

A smooth dynamic network model for patent collaboration data

The development and application of models, which take the evolution of network dynamics into account are receiving increasing attention. We contribute to this field and focus on a profile likelihood approach to model time-stamped event data for a large-scale dynamic network. We investigate the collaboration of inventors using EU patent data. As event we consider the submission of a joint patent and we explore the driving forces for collaboration between inventors. We propose a flexible semiparametric model, which includes external and internal covariates, where the latter are built from the network history.

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arXiv2020

Mixture Models and Networks -- Overview of Stochastic Blockmodelling

Mixture models are probabilistic models aimed at uncovering and representing latent subgroups within a population. In the realm of network data analysis, the latent subgroups of nodes are typically identified by their connectivity behaviour, with nodes behaving similarly belonging to the same community. In this context, mixture modelling is pursued through stochastic blockmodelling. We consider stochastic blockmodels and some of their variants and extensions from a mixture modelling perspective. We also survey some of the main classes of estimation methods available, and propose an alternative approach. In addition to the discussion of inferential properties and estimating procedures, we focus on the application of the models to several real-world network datasets, showcasing the advantages and pitfalls of different approaches.

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arXiv2020

Intensity Estimation on Geometric Networks with Penalized Splines

In the past decades, the growing amount of network data has lead to many novel statistical models. In this paper we consider so called geometric networks. Typical examples are road networks or other infrastructure networks. But also the neurons or the blood vessels in a human body can be interpreted as a geometric network embedded in a three-dimensional space. In all these applications a network specific metric rather than the Euclidean metric is usually used, which makes the analyses on network data challenging. We consider network based point processes and our task is to estimate the intensity (or density) of the process which allows to detect high- and low- intensity regions of the underlying stochastic processes. Available routines that tackle this problem are commonly based on kernel smoothing methods. However, kernel based estimation in general exhibits some drawbacks such as suffering from boundary effects and the locality of the smoother. In an Euclidean space, the disadvantages of kernel methods can be overcome by using penalized spline smoothing. We here extend penalized spline smoothing towards smooth intensity estimation on geometric networks and apply the approach to both, simulated and real world data. The results show that penalized spline based intensity estimation is numerically efficient and outperforms kernel based methods. Furthermore, our approach easily allows to incorporate covariates, which allows to respect the network geometry in a regression model framework.

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arXiv2020

Estimation of Latent Network Flows in Bike-Sharing Systems

Estimation of latent network flows is a common problem in statistical network analysis. The typical setting is that we know the margins of the network, i.e. in- and outdegrees, but the flows are unobserved. In this paper, we develop a mixed regression model to estimate network flows in a bike-sharing network if only the hourly differences of in- and outdegrees at bike stations are known. We also include exogenous covariates such as weather conditions. Two different parameterizations of the model are considered to estimate 1) the whole network flow and 2) the network margins only. The estimation of the model parameters is proposed via an iterative penalized maximum likelihood approach. This is exemplified by modeling network flows in the Vienna Bike-Sharing Network. Furthermore, a simulation study is conducted to show the performance of the model. For practical purposes it is crucial to predict when and at which station there is a lack or an excess of bikes. For this application, our model shows to be well suited by providing quite accurate predictions.

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arXiv2019

A Dynamic Separable Network Model with Actor Heterogeneity: An Application to Global Weapons Transfers

In this paper we propose to extend the separable temporal exponential random graph model (STERGM) to account for time-varying network- and actor-specific effects. Our application case is the network of international major conventional weapons transfers, based on data from the Stockholm International Peace Research Institute (SIPRI). The application is particularly suitable since it allows to distinguish the potentially differing driving forces for creating new trade relationships and for the endurance of existing ones. In accordance with political economy models we expect security- and network-related covariates to be most important for the formation of transfers, whereas repeated transfers should prevalently be determined by the receivers' market size and military spending. Our proposed modelling approach corroborates the hypothesis and quantifies the corresponding effects. Additionally, we subject the time-varying heterogeneity effects to a functional principal component analysis. This serves as exploratory tool and allows to identify countries that stand out by exceptional increases or decreases of their tendency to import and export weapons.

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arXiv2019

Regression-based Network Reconstruction with Nodal and Dyadic Covariates and Random Effects

Network (or matrix) reconstruction is a general problem which occurs if the margins of a matrix are given and the matrix entries need to be predicted. In this paper we show that the predictions obtained from the iterative proportional fitting procedure (IPFP) or equivalently maximum entropy (ME) can be obtained by restricted maximum likelihood estimation relying on augmented Lagrangian optimization. Based on the equivalence we extend the framework of network reconstruction towards regression by allowing for exogenous covariates and random heterogeneity effects. The proposed estimation approach is compared with different competing methods for network reconstruction and matrix estimation. Exemplary, we apply the approach to interbank lending data, provided by the Bank for International Settlement (BIS). This dataset provides full knowledge of the real network and is therefore suitable to evaluate the predictions of our approach. It is shown that the inclusion of exogenous information allows for superior predictions in terms of $L_1$ and $L_2$ errors. Additionally, the approach allows to obtain prediction intervals via bootstrap that can be used to quantify the uncertainty attached to the predictions.

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arXiv2019

In Search of Lost Edges: A Case Study on Reconstructing Financial Networks

To capture the systemic complexity of international financial systems, network data is an important prerequisite. However, dyadic data is often not available, raising the need for methods that allow for reconstructing networks based on limited information. In this paper, we are reviewing different methods that are designed for the estimation of matrices from their marginals and potentially exogenous information. This includes a general discussion of the available methodology that provides edge probabilities as well as models that are focussed on the reconstruction of edge values. Besides summarizing the advantages, shortfalls and computational issues of the approaches, we put them into a competitive comparison using the SWIFT (Society for Worldwide Interbank Financial Telecommunication) MT 103 payment messages network (MT 103: Single Customer Credit Transfer). This network is not only economically meaningful but also fully observed which allows for an extensive competitive horse race of methods. The comparison concerning the binary reconstruction is divided into an evaluation of the edge probabilities and the quality of the reconstructed degree structures. Furthermore, the accuracy of the predicted edge values is investigated. To test the methods on different topologies, the application is split into two parts. The first part considers the full MT 103 network, being an illustration for the reconstruction of large, sparse financial networks. The second part is concerned with reconstructing a subset of the full network, representing a dense medium-sized network. Regarding substantial outcomes, it can be found that no method is superior in every respect and that the preferred model choice highly depends on the goal of the analysis, the presumed network structure and the availability of exogenous information.

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arXiv2019

Tempus Volat, Hora Fugit -- A Survey of Tie-Oriented Dynamic Network Models in Discrete and Continuous Time

Given the growing number of available tools for modeling dynamic networks, the choice of a suitable model becomes central. The goal of this survey is to provide an overview of tie-oriented dynamic network models. The survey is focused on introducing binary network models with their corresponding assumptions, advantages, and shortfalls. The models are divided according to generating processes, operating in discrete and continuous time. First, we introduce the Temporal Exponential Random Graph Model (TERGM) and the Separable TERGM (STERGM), both being time-discrete models. These models are then contrasted with continuous process models, focusing on the Relational Event Model (REM). We additionally show how the REM can handle time-clustered observations, i.e., continuous time data observed at discrete time points. Besides the discussion of theoretical properties and fitting procedures, we specifically focus on the application of the models on two networks that represent international arms transfers and email exchange. The data allow to demonstrate the applicability and interpretation of the network models.

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arXiv2019

Censored Regression for Modelling International Small Arms Trading and its "Forensic" Use for Exploring Unreported Trades

In this paper we use a censored regression model to investigate data on the international trade of small arms and ammunition (SAA) provided by the Norwegian Initiative on Small Arms Transfers (NISAT). Taking a network based view on the transfers, we not only rely on exogenous covariates but also estimate endogenous network effects. We apply a spatial autocorrelation (SAR) model with multiple weight matrices. The likelihood is maximized employing the Monte Carlo Expectation Maximization (MCEM) algorithm. Our approach reveals strong and stable endogenous network effects. Furthermore, we find evidence for a substantial path dependence as well as a close connection between exports of civilian and military small arms. The model is then used in a "forensic" manner to analyse latent network structures and thereby to identify countries with higher or lower tendency to export or import than reflected in the data. The approach is also validated using a simulation study.

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arXiv2018

Exploring Dependence Structures in the International Arms Trade Network

In the paper we analyse dependence structures among international trade flows of major conventional weapons from 1952 to 2016. We employ a Network Disturbance Model commonly used in inferential network analysis and spatial econometrics. The dependence structure is represented by pre-defined weight matrices that allow for relating the arms trade flows from the network of international arms exchange. Several different weight matrices are compared by means of the AIC in order to select the best dependence structure. It turns out that the dependence structure among the arms trade flows is rather complex and can be represented by a specification that, simply speaking, relates each arms trade flow to all exports and imports of the sending and the receiving state. By controlling for explanatory variables we are able to show the influence of political and economic variables on the volume traded.

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arXiv2016

Stable Exponential Random Graph Models with Non-parametric Components for Large Dense Networks

Exponential Random Graph Models (ERGM) behave peculiar in large networks with thousand(s) of actors (nodes). Standard models containing two-star or triangle counts as statistics are often unstable leading to completely full or empty networks. Moreover, numerical methods break down which makes it complicated to apply ERGMs to large networks. In this paper we propose two strategies to circumvent these obstacles. First, we fit a model to a subsampled network and secondly, we show how linear statistics (like two-stars etc.) can be replaced by smooth functional components. These two steps in combination allow to fit stable models to large network data, which is illustrated by a data example including a residual analysis.

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arXiv2015

Bayesian Exponential Random Graph Models with Nodal Random Effects

We extend the well-known and widely used Exponential Random Graph Model (ERGM) by including nodal random effects to compensate for heterogeneity in the nodes of a network. The Bayesian framework for ERGMs proposed by Caimo and Friel (2011) yields the basis of our modelling algorithm. A central question in network models is the question of model selection and following the Bayesian paradigm we focus on estimating Bayes factors. To do so we develop an approximate but feasible calculation of the Bayes factor which allows one to pursue model selection. Two data examples and a small simulation study illustrate our mixed model approach and the corresponding model selection.

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arXiv2012

Mixtures of g-Priors for Generalised Additive Model Selection with Penalised Splines

We propose an objective Bayesian approach to the selection of covariates and their penalised splines transformations in generalised additive models. Specification of a reasonable default prior for the model parameters and combination with a multiplicity-correction prior for the models themselves is crucial for this task. Here we use well-studied and well-behaved continuous mixtures of g-priors as default priors. We introduce the methodology in the normal model and extend it to non-normal exponential families. A simulation study and an application from the literature illustrate the proposed approach. An efficient implementation is available in the R-package "hypergsplines".

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