La explicación a través de la visualización de redes

Las evaluaciones de causa y efecto, configuraciones y dinámicas están en el corazón de nuestro pensamiento y nuestras explicaciones (Tufte 1997: 9). Aunque se han desarrollado muchos métodos para dichas evaluaciones, que son utilizados en nuestra práctica científica diaria, es frecuente que la visualización no sea considerada uno de ellos. En este artículo, defendemos en primer lugar que ésto se debe a la práctica habitual de visualizar los datos en lugar de la información contenida en los mismos. En segundo lugar, repasamos una serie de principios para la visualización efectiva. Tercero, valoramos algunos ejemplos específicos de la visualización de redes basada en estos principios. Cuarto, demostramos que la visualización de información aplicada a redes no sólo es un posible método para el análisis de configuraciones, dinámicas y causas, sino que, además, tiene un valor añadido en comparación con otros métodos. Finalmente, concluimos con algunas implicaciones para la visualización de redes sociales.Assessments of configurations, dynamics, and cause and effect are at the heart of our thinking and explanation. Although numerous methods for such assessments have been developed and are being used in our daily scientific practice, visualization is frequently not considered one of them. In this paper we will first argue that this is due to the common practice of visualizing data rather than the information contained in it; secondly, we address a number of principles for effective visualization; thirdly, we assess specific examples for the visualization of networks on these principles; fourthly, we demonstrate that information visualization applied to networks is not only a possible method to analyze configurations, dynamics, and cause but, moreover, that it has an added value compared to other methods. We conclude with implications for the visualization of social networks

Stop Simulating! Efficient Computation of Tournament Winning Probabilities

In the run-up to any major sports tournament, winning probabilities of participants are publicized for engagement and betting purposes. These are generally based on simulating the tournament tens of thousands of times by sampling from single-match outcome models. We show that, by virtue of the tournament schedule, exact computation of winning probabilties can be substantially faster than their approximation through simulation. This notably applies to the 2022 and 2023 FIFA World Cup Finals, and is independent of the model used for individual match outcomes.Comment: Working paper; first draft published prior to WWC202

La explicación a través de la visualización de redes

Assessments of configurations, dynamics, and cause and effect are at the heart of our thinking and explanation. Although numerous methods for such assessments have been developed and are being used in our daily scientific practice, visualization is frequently not considered one of them. In this paper we will first argue that this is due to the common practice of visualizing data rather than the information contained in it; secondly, we address a number of principles for effective visualization; thirdly, we assess specific examples for the visualization of networks on these principles; fourthly, we demonstrate that information visualization applied to networks is not only a possible method to analyze configurations, dynamics, and cause but, moreover, that it has an added value compared to other methods. We conclude with implications for the visualization of social networks.Las evaluaciones de causa y efecto, configuraciones y dinámicas están en el corazón de nuestro pensamiento y nuestras explicaciones (Tufte 1997: 9). Aunque se han desarrollado muchos métodos para dichas evaluaciones, que son utilizados en nuestra práctica científica diaria, es frecuente que la visualización no sea considerada uno de ellos. En este artículo, defendemos en primer lugar que ésto se debe a la práctica habitual de visualizar los datos en lugar de la información contenida en los mismos. En segundo lugar, repasamos una serie de principios para la visualización efectiva. Tercero, valoramos algunos ejemplos específicos de la visualización de redes basada en estos principios. Cuarto, demostramos que la visualización de información aplicada a redes no sólo es un posible método para el análisis de configuraciones, dinámicas y causas, sino que, además, tiene un valor añadido en comparación con otros métodos. Finalmente, concluimos con algunas implicaciones para la visualización de redes sociales

Social Capital Companion : capturing Personal Networks as They are Lived

Personal networks are complex variables characterizing the social resources and constraints of individuals and can be used to explain outcome such as behaviour, health, or job performance. Collecting qualitatively rich personal network data, however, is costly and difficulties increase if longitudinal data is needed. In this paper we identify requirements that collected personal network data should satisfy to be suitable for longitudinal analysis. Likewise we identify requirements on the tools for collecting personal networks and study which of these are satisfied by current data collection tools. We present design aspects and a prototypical implementation of a smartphone app (dubbed the Social Capital Companion) allowing users to keep a personal network diary on their own mobile devices over potentially long periods of time

Modeling frequency and type of interaction in event networks

Longitudinal social networks are increasingly given by event data; i.e., data coding the time and type of interaction between social actors. Examples include networks stemming from computer-mediated communication, open collaboration in wikis, phone call data and interaction among political actors. In this paper, we propose a general model for networks of dyadic, typed events. We decompose the probability of events into two components: the first modeling the frequency of interaction and the second modeling the conditional event type, i. e., the quality of interaction, given that interaction takes place. While our main contribution is methodological, for illustration we apply our model to data about political cooperation and conficts collected with the Kansas Event Data System. Special emphasis is given to the fact that some explanatory variables affect the frequency of interaction while others rather determine the level of cooperativeness vs. hostility, if interaction takes place. Furthermore, we analyze if and how model components controlling for network dependencies affect findings on the effects of more traditional predictors such as geographic proximity or joint alliance membership. We argue that modeling the conditional event type is a valuable – and in some cases superior – alternative to previously proposed models for networks of typed events

On Dasgupta’s Hierarchical Clustering Objective and Its Relation to Other Graph Parameters

Postponed access: the file will be available after 2022-09-09The minimum height of vertex and edge partition trees are well-studied graph parameters known as, for instance, vertex and edge ranking number. While they are NP-hard to determine in general, linear-time algorithms exist for trees. Motivated by a correspondence with Dasgupta’s objective for hierarchical clustering we consider the total rather than maximum depth of vertices as an alternative objective for minimization. For vertex partition trees this leads to a new parameter with a natural interpretation as a measure of robustness against vertex removal. As tools for the study of this family of parameters we show that they have similar recursive expressions and prove a binary tree rotation lemma. The new parameter is related to trivially perfect graph completion and therefore intractable like the other three are known to be. We give polynomial-time algorithms for both total-depth variants on caterpillars and on trees with a bounded number of leaf neighbors. For general trees, we obtain a 2-approximation algorithm.acceptedVersio