Predicting human mobility through the assimilation of social media traces into mobility models

Predicting human mobility flows at different spatial scales is challenged by the heterogeneity of individual trajectories and the multi-scale nature of transportation networks. As vast amounts of digital traces of human behaviour become available, an opportunity arises to improve mobility models by integrating into them proxy data on mobility collected by a variety of digital platforms and location-aware services. Here we propose a hybrid model of human mobility that integrates a large-scale publicly available dataset from a popular photo-sharing system with the classical gravity model, under a stacked regression procedure. We validate the performance and generalizability of our approach using two ground-truth datasets on air travel and daily commuting in the United States: using two different cross-validation schemes we show that the hybrid model affords enhanced mobility prediction at both spatial scales.Comment: 17 pages, 10 figure

Geolocalización de usuarios en Twitter utilizando redes convolucionales de grafos

En este trabajo utilizamos un conjunto de datos recolectados de la red social Twitter con el objetivo de analizar el desempeño de distintos modelos que proponemos para determinar la geolocalización de los usuarios de la plataforma. También realizamos un análisis sobre los perfiles de los usuarios para verificar qué tan fiable puede ser la determinación de su residencia. En el artículo detallamos distintas formas de construir las redes que modelan las relaciones entre los usuarios a fin de mejorar la estimación de su ubicación, con sus respectivas ventajas y desventajas. Por último, explicamos nuestro procedimiento para la detección de términos locales, y la conformación de secuencias para los métodos basados en redes neuronales.Sociedad Argentina de Informática e Investigación Operativ

Router-level community structure of the Internet Autonomous Systems

The Internet is composed of routing devices connected between them and organized into independent administrative entities: the Autonomous Systems. The existence of different types of Autonomous Systems (like large connectivity providers, Internet Service Providers or universities) together with geographical and economical constraints, turns the Internet into a complex modular and hierarchical network. This organization is reflected in many properties of the Internet topology, like its high degree of clustering and its robustness. In this work, we study the modular structure of the Internet router-level graph in order to assess to what extent the Autonomous Systems satisfy some of the known notions of community structure. We show that the modular structure of the Internet is much richer than what can be captured by the current community detection methods, which are severely affected by resolution limits and by the heterogeneity of the Autonomous Systems. Here we overcome this issue by using a multiresolution detection algorithm combined with a small sample of nodes. We also discuss recent work on community structure in the light of our results