arXiv subjects

Jürgen Lerner

Publications and source records attributed to Jürgen Lerner.

11 recordsLinked to original sources

arXiv2026

Modeling Tripartite Hyperevents in Scientific Collaboration Networks

Sociological research has framed collective action in science, innovation, and culture as tripartite networks connecting teams of actors, lists of prior works, and sets of labels (e.g., keywords, topics). While methods for multipartite social networks were proposed decades ago, and have received a recent surge in interest, none of the suggested solutions scale to the size and granularity of contemporary data sets (scientific publications, patents, filmmaking) and at the same time allow for testing multiple competing hypotheses about the drivers of collective production. In this paper, we address this gap by applying Relational Hyperevent Models (RHEM) to dynamic tripartite hypergraphs. Using scientific networks as a case study, we model events linking any number of actors, references, and keywords, testing and controlling for inter-dependencies within and between each set.

stat.AP↗

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

Modeling temporal hypergraphs

Networks representing social, biological, technological or other systems are often characterized by higher-order interaction involving any number of nodes. Temporal hypergraphs are given by ordered sequences of hyperedges representing sets of nodes interacting at given points in time. In this paper we discuss how a recently proposed model family for time-stamped hyperedges - relational hyperevent models (RHEM) - can be employed to define tailored null distributions for temporal hypergraphs and to test and control for complex dependencies in hypergraph dynamics. RHEM can be specified with a given vector of temporal hyperedge statistics - functions that quantify the structural position of hyperedges in the history of previous hyperedges - and equate expected values of these statistics with their empirically observed values. This allows, for instance, to analyze the overrepresentation or underrepresentation of temporal hyperedge configurations in a model that reproduces the observed distributions of possibly complex sub-configurations, including but going beyond node degrees. Concrete examples include, but are not limited to, preferential attachment, repetition of subsets of any given size, triadic closure, homophily, and degree assortativity for subsets of any order.

physics.soc-ph↗

arXiv2025

Comparing name generator designs in rural panel studies: analyzing alter retention and change

We conducted a two-wave personal network study in a rural Romanian community, interviewing the same participants (n = 68) using two name generators. Wave 1 employed a fixed-choice generator (n = 25) focused on emotional closeness; Wave 2 used a free-choice generator based on frequent interaction. We compared tie characteristics and assessed retention across waves. Alters who were kin, co-residents, or emotionally close were more likely to be retained, regardless of generator type. These findings underscore the role of relational attributes in personal network stability and highlight design considerations for network studies in resource-limited, culturally distinct settings.

stat.ME↗

arXiv2025

Socio-cognitive Networks between Researchers: Investigating Scientific Dualities with the Group-Oriented Relational Hyperevent Model

Understanding why researchers cite certain works remains a key question in the study of scientific networks. Prior research has identified factors such as relevance, group cohesion, and source crediting. However, the interplay between cognitive and social dimensions in citation behavior - often conceptualized as a socio-cognitive network - is frequently overlooked, particularly regarding the intermediary steps that lead to a citation. Since a citation first requires a work to be published by a set of authors, we examine how the structure of coauthorship networks influences citation patterns. To investigate this relationship, we analyze the citation and collaboration behavior of Chilean astronomers from 2013 to 2015 using the Group-Oriented Relational Hyperevent Model, which allows us to study coauthorship and citation networks in a joint framework. Our findings suggest that when selecting which works to cite, authors favor recent research and maintain cognitive continuity across cited works. At the same time, we observe that coherent groups - closely connected coauthors - tend to be co-cited more frequently in subsequent publications, reinforcing the interdependence of collaboration and citation networks.

cs.SI↗

arXiv2024

Cross-sectional personal network analysis of adult smoking in rural areas

While research on adolescent smoking is extensive, little attention has been given to smoking behaviors among rural middle-aged and older adults. This study examines the role of personal networks and sociodemographic factors in predicting smoking status in a rural Romanian community. Using a link-tracing sampling method, we gathered data from 76 participants out of 83 in Leresti, Arges County. Face-to-face interviews collected sociodemographic data and network information, including smoking status and relational dynamics. We applied multilevel logistic regression models to predict smoking behaviors (current smokers, former smokers, and non-smokers) based on individual characteristics and network influences. Results indicate that social networks significantly influence smoking behaviors. For current smokers, having a smoking family member greatly increased the odds of smoking (OR = 2.51, 95% CI: 1.62, 3.91, p < 0.001). Similarly, non-smoking family members increased the likelihood of being a non-smoker (OR = 1.64, 95% CI: 1.04, 2.61, p < 0.05). Women were less likely to smoke, highlighting sex differences in behavior. These findings emphasize the critical role of social networks in shaping smoking habits, advocating for targeted interventions in rural areas.

stat.AP↗

arXiv2024

Relational hyperevent models for the coevolution of coauthoring and citation networks

The development of suitable statistical models for the analysis of bibliographic networks has trailed behind the empirical ambitions expressed by recent studies of science of science. Extant research typically restricts the analytical focus to either paper citation networks, or author collaboration networks. These networks involve not only direct relationships between papers or authors, but also a broader system of dependencies between the references of papers connected through multiple simultaneous citation links. In this work, we extend recently developed relational hyperevent models (RHEM) to analyze scientific networks - systems of scientific publications connected by citations and authorship. We introduce new covariates that represent theoretically relevant and empirically meaningful sub-network configurations. The new model specification supports testing of hypotheses that align with the polyadic nature of scientific publication events and the multiple interdependencies between authors and references of current and prior papers. We implement the model using open-source software to analyze a large, publicly available scientific network dataset. A significant finding of the study is the tendency for subsets of papers to be repeatedly cited together across publications. This result is crucial as it suggests that the papers' impact may be partly due to endogenous network processes. More broadly, the study shows that models accounting for both the hyperedge structure of publication events and the interconnections between authors and references significantly enhance our understanding of the network mechanisms that drive scientific production, productivity, and impact.

cs.DL↗

arXiv2022

Relational hyperevent models for polyadic interaction networks

Polyadic, or "multicast" social interaction networks arise when one sender addresses multiple receivers simultaneously. Currently available relational event models (REM) are not well suited to the analysis of polyadic interaction networks because they specify event rates for sets of receivers as functions of dyadic covariates associated with the sender and one receiver at a time. Relational hyperevent models (RHEM) address this problem by specifying event rates as functions of hyperedge covariates associated with the sender and the entire set of receivers. For instance, hyperedge covariates can express the tendency of senders to repeatedly address the same pairs (or larger sets) of receivers - a simple and frequent pattern in polyadic interaction data which, however, cannot be expressed with dyadic covariates. In this article we demonstrate the potential benefits of RHEMs for the analysis of polyadic social interaction. We define and discuss practically relevant effects that are not available for REMs but may be incorporated in empirical specifications of RHEM. We illustrate the empirical value of RHEM, and compare them with related REM, in a reanalysis of the canonical Enron email data.

stat.AP↗

arXiv2021

Micro-level network dynamics of scientific collaboration and impact: relational hyperevent models for the analysis of coauthor networks

We discuss a recently proposed family of statistical network models - relational hyperevent models (RHEM) - for analyzing team selection and team performance in scientific coauthor networks. The underlying rationale for using RHEM in studies of coauthor networks is that scientific collaboration is intrinsically polyadic, that is, it typically involves teams of any size. Consequently, RHEM specify publication rates associated with hyperedges representing groups of scientists of any size. Going beyond previous work on RHEM for meeting data, we adapt this model family to settings in which relational hyperevents have a dedicated outcome, such as a scientific paper with a measurable impact (e.g., the received number of citations). Relational outcome can on the one hand be used to specify additional explanatory variables in RHEM since the probability of coauthoring may be influenced, for instance, by prior (shared) success of scientists. On the other hand relational outcome can also serve as a response variable in models seeking to explain the performance of scientific teams. To tackle the latter we propose relational hyperevent outcome models (RHOM) that are closely related with RHEM to the point that both model families can specify the likelihood of scientific collaboration - and the expected performance, respectively - with the same set of explanatory variables allowing to assess, for instance, whether variables leading to increased collaboration also tend to increase scientific impact. For illustration, we apply RHEM to empirical coauthor networks comprising more than 350,000 published papers by scientists working in three scientific disciplines.

stat.ME↗

arXiv2019

REM beyond dyads: relational hyperevent models for multi-actor interaction networks

We introduce relational hyperevent models (RHEM) as a generalization of relational event models to events occurring on hyperedges involving any number of actors. RHEM can specify time-varying event rates for the full space of directed or undirected hyperedges and can be applied to model, among others, meetings, team assembly, team performance, or multi-actor communication. We illustrate the newly proposed model on two empirical hyperevent networks about meetings of government ministers and co-authoring of scientific papers.

cs.SI↗

arXiv2019

Reliability of relational event model estimates under sampling: how to fit a relational event model to 360 million dyadic events

We assess the reliability of relational event model parameters estimated under two sampling schemes: (1) uniform sampling from the observed events and (2) case-control sampling which samples non-events, or null dyads ("controls"), from a suitably defined risk set. We experimentally determine the variability of estimated parameters as a function of the number of sampled events and controls per event, respectively. Results suggest that relational event models can be reliably fitted to networks with more than 12 million nodes connected by more than 360 million dyadic events by analyzing a sample of some tens of thousands of events and a small number of controls per event. Using data that we collected on the Wikipedia editing network, we illustrate how network effects commonly included in empirical studies based on relational event models need widely different sample sizes to be estimated reliably. For our analysis we use an open-source software which implements the two sampling schemes, allowing analysts to fit and analyze relational event models to the same or other data that may be collected in different empirical settings, varying sample parameters or model specification.

cs.SI↗