Separable and semiparametric network-based counting processes applied to the international combat aircraft trades

Fritz, Cornelius; Thurner, Paul W.; Kauermann, Göran

doi:10.1017/nws.2021.9

Cited by 12 publications

(5 citation statements)

References 78 publications

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“…Graphs are a mathematical description of the entities and their relationships in a given domain and naturally arise in a variety of seemingly distant fields, for instance, biology 50 , political science 51 , and economics 52 . However, due to their non-euclidean structure, it is not straightforward to apply traditional machine learning methods to problems involving graphs, as these methods operate on vectors in for some 22 .…”

Section: Methodological Backgroundmentioning

confidence: 99%

Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany

Fritz

Dorigatti

Rügamer

2022

Sci Rep

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During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease incidents are a vital tool for policymakers to manage healthcare resources. In this context, several experts have called for the necessity to account for human mobility to explain the spread of COVID-19. Existing approaches often apply standard models of the respective research field, frequently restricting modeling possibilities. For instance, most statistical or epidemiological models cannot directly incorporate unstructured data sources, including relational data that may encode human mobility. In contrast, machine learning approaches may yield better predictions by exploiting these data structures yet lack intuitive interpretability as they are often categorized as black-box models. We propose a combination of both research directions and present a multimodal learning framework that amalgamates statistical regression and machine learning models for predicting local COVID-19 cases in Germany. Results and implications: the novel approach introduced enables the use of a richer collection of data types, including mobility flows and colocation probabilities, and yields the lowest mean squared error scores throughout the observational period in the reported benchmark study. The results corroborate that during most of the observational period more dispersed meeting patterns and a lower percentage of people staying put are associated with higher infection rates. Moreover, the analysis underpins the necessity of including mobility data and showcases the flexibility and interpretability of the proposed approach.

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Section: Methodological Backgroundmentioning

confidence: 99%

Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany

Fritz

Dorigatti

Rügamer

2022

Sci Rep

Self Cite

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“…A straightforward refinement along the lines of would be to include windowed effects, that is, endogenous statistics that are only using history ranging into the past for a specific duration, or exogenous covariates calculated from additional networks to the one modeled. The first modification could also be extended to separable models as proposed in Fritz et al (2021). A relatively simplistic version of the latter type of covariate was incorporated in Section 4.2 to account for common friendships but more complex covariates are possible.…”

Section: Discussionmentioning

confidence: 99%

All that glitters is not gold: Relational events models with spurious events

Fritz

Mehrl²,

Thurner³

et al. 2022

Net Sci

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As relational event models are an increasingly popular model for studying relational structures, the reliability of large-scale event data collection becomes more and more important. Automated or human-coded events often suffer from non-negligible false-discovery rates in event identification. And most sensor data are primarily based on actors’ spatial proximity for predefined time windows; hence, the observed events could relate either to a social relationship or random co-location. Both examples imply spurious events that may bias estimates and inference. We propose the Relational Event Model for Spurious Events (REMSE), an extension to existing approaches for interaction data. The model provides a flexible solution for modeling data while controlling for spurious events. Estimation of our model is carried out in an empirical Bayesian approach via data augmentation. Based on a simulation study, we investigate the properties of the estimation procedure. To demonstrate its usefulness in two distinct applications, we employ this model to combat events from the Syrian civil war and student co-location data. Results from the simulation and the applications identify the REMSE as a suitable approach to modeling relational event data in the presence of spurious events.

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“…A number of additional methodological advancements and analytical tools have been proposed recently. Some researchers discussed temporal heterogeneity of parameters (Bauer et al, 2021;Meijerink-Bosman et al, 2022;Fritz et al, 2021), making use of the wealth of information typically available in relational event studies. Some work was concerned with relational events that are not purely dyadic and discussed multicast, group-related, and hyper event models (Perry & Wolfe, 2013;Hoffman et al, 2020;Lerner & Lomi, 2022.…”

Section: Relational Event Modelsmentioning

confidence: 99%

Relational event models in network science

Butts

Lomi

Snijders

et al. 2023

Net Sci

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Relational event models (REMs) for the analysis of social interaction were first introduced 15 years ago. Since then, a number of important substantive and methodological contributions have produced their progressive refinement and hence facilitated their increased adoption in studies of social and other networks. Today REMs represent a well-established class of statistical models for relational processes. This special issue of Network Science demonstrates the standing and recognition that REMs have achieved within the network analysis and networks science communities. We wrote this brief introductory editorial essay with four main objectives in mind: (i) positioning relational event data and models in the larger context of contemporary network science and social network research; (ii) reviewing some of the most important recent developments; (iii) presenting the innovative studies collected in this special issue as evidence of the empirical value of REMs, and (iv) identifying open questions and future research directions.

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Separable and semiparametric network-based counting processes applied to the international combat aircraft trades

Cited by 12 publications

References 78 publications

Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany

Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany

All that glitters is not gold: Relational events models with spurious events

Relational event models in network science

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