arXiv subjects

Martina Boschi

Publications and source records attributed to Martina Boschi.

5 recordsLinked to original sources

arXiv2026

Introduction to Relational Event Modelling

Interactions and time shape many aspects of life. Everyday activities -- like conversations, emails, money transfers, citations, and even acts of violence -- are relational events: interactions between a sender and a receiver at a specific moment. At the intersection of event-history analysis and network modelling, relational event models (REMs) offer a powerful framework for studying when and why these events occur. Recent advances have made it possible to express REMs as generalized additive models, allowing researchers to capture complex, non-linear patterns over time. A hands-on guide to REMs is still missing. This tutorial fills that gap. It provides a practical introduction to REMs, incorporating the latest developments in the field. It demonstrates how to simulate synthetic relational-event data and walks through several empirical applications, comparing different modeling and inference strategies. By bringing together theory, simulation, and application, this tutorial lowers the barrier to entry and makes REMs a more accessible and practical tool.

stat.ME↗

arXiv2025

Beyond Linearity and Time-Homogeneity: Relational Hyper Event Models with Time-Varying Non-Linear Effects

Recent technological advances have made it easier to collect large and complex networks of time-stamped relational events connecting two or more entities. Relational hyper-event models (RHEMs) aim to explain the dynamics of these events by modeling the event rate as a function of statistics based on past history and external information. However, despite the complexity of the data, most current RHEM approaches still rely on a linearity assumption to model this relationship. In this work, we address this limitation by introducing a more flexible model that allows the effects of statistics to vary non-linearly and over time. While time-varying and non-linear effects have been used in relational event modeling, we take this further by modeling joint time-varying and non-linear effects using tensor product smooths. We validate our methodology on both synthetic and empirical data. In particular, we use RHEMs to study how patterns of scientific collaboration and impact evolve over time. Our approach provides deeper insights into the dynamic factors driving relational hyper-events, allowing us to evaluate potential non-monotonic patterns that cannot be identified using linear models.

stat.ME↗

arXiv2025

Mixed additive modelling of global alien species co-invasions of plants and insects

Alien species refer to non-native species introduced by humans into an ecosystem, which can cause harm to the environment, economy, or human health. Although there is considerable literature on the subject, the presence of confounding factors has so far prevented a comprehensive picture of the relative importance of various drivers of such invasions. In this manuscript, we aim to develop and apply a general mixed additive relational event model to describe the pattern of global invasions of alien species. The diffusion of alien species can be regarded as a relational event, where the species -- the sender -- reaches a region -- the receiver -- at a specific time in history. We use the First Record Database, which contains all co-invasions by insects and plants between 1880 and 2005. A relational event model (REM) is employed to describe the underlying hazard of each species-region pair. Besides potentially time-varying, exogenous, and endogenous covariates, the mixed additive REM incorporates time-varying and random effects, allowing for taxa-specific baseline rates while accounting for the potential synergistic effect between plants and insects in the invasion process. Our efficient inference procedure relies on case-control sampling, yielding the same likelihood as that of a degenerate logistic regression. We propose fitting the mixed additive REM via a generalised additive model with random effects as 0-dimensional splines. The resulting computational efficiency means that complex models for large dynamic networks can be estimated in seconds on a standard computer. Furthermore, we present a framework for testing the goodness-of-fit of our mixed additive REM for the invasions by vascular plants and insects by means of cumulative martingale-residuals. Implementation is performed through the R package mgcv.

stat.AP↗

arXiv2024

Goodness of fit of relational event models

A type of dynamic network involves temporally ordered interactions between actors, where past network configurations may influence future ones. The relational event model can be used to identify the underlying dynamics that drive interactions among system components. Despite the rapid development of this model over the past 15 years, an ongoing area of research revolves around evaluating the goodness of fit of this model, especially when it incorporates time-varying and random effects. Current methodologies often rely on comparing observed and simulated events using specific statistics, but this can be computationally intensive, and requires various assumptions. We propose an additive mixed-effect relational event model estimated via case-control sampling, and introduce a versatile framework for testing the goodness of fit of such models using weighted martingale residuals. Our focus is on a Kolmogorov-Smirnov type test designed to assess if covariates are accurately modeled. Our approach can be easily extended to evaluate whether other features of network dynamics have been appropriately incorporated into the model. We assess the goodness of fit of various relational event models using synthetic data to evaluate the test's power and coverage. Furthermore, we apply the method to a social study involving 57,791 emails sent by 159 employees of a Polish manufacturing company in 2010. The method is implemented in the R package mgcv.

stat.ME↗

arXiv2022

Nonlinear oscillators via Čebyšëv quintic approximations

Aim of this work is the study of differential equations governing non--dissipative non--linear oscillators; these arise in different physical models such as the treatment of relativistic oscillators, up to generalizations to Duffing's relativistic oscillators and in non--relativistic models which deals with cables with an attached midpoint mass, or some harmonic Duffing oscillators.

math.CA↗