The Strength of Friendship Ties in Proximity Sensor Data

Understanding how people interact and socialize is important in many contexts from disease control to urban planning. Datasets that capture this specific aspect of human life have increased in size and availability over the last few years. We have yet to understand, however, to what extent such electronic datasets may serve as a valid proxy for real life social interactions. For an observational dataset, gathered using mobile phones, we analyze the problem of identifying transient and non-important links, as well as how to highlight important social interactions. Applying the Bluetooth signal strength parameter to distinguish between observations, we demonstrate that weak links, compared to strong links, have a lower probability of being observed at later times, while such links--on average--also have lower link-weights and probability of sharing an online friendship. Further, the role of link-strength is investigated in relation to social network properties.Comment: Updated Introduction, added references. 12 pages, 7 figure

Design process robustness: A bi-partite network analysis reveals the central importance of people

Design processes require the joint effort of many people to collaborate and work on multiple activities. Effective techniques to analyse and model design processes are important for understanding organisational dynamics, for improving collaboration, and for planning robust design processes, reducing the risk of rework and delays. Although there has been much progress in modelling and understanding design processes, little is known about the interplay between people and the activities they perform and its influence on design process robustness. To analyse this interplay, we model a large-scale design process of a biomass power plant with people and activities as a bipartite network. Observing that some people act as bridges between activities organised to form nearly independent modules, in order to evaluate process fragility, we simulate random failures and targeted attacks to people and activities. We find that our process is more vulnerable to attacks to people rather than activities. These findings show how the allocation of people to activities can obscure an inherent fragility, making the process highly sensitive and dependent on specific people. More generally, we show that the behaviour of robustness is determined by the degree distributions, the heterogeneity of which can be leveraged to improve robustness and resilience to cascading failures. Overall, we show that it is important to carefully plan the assignment of people to activities

Understanding predictability and exploration in human mobility

Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility

Desarrollo de un sistema de seguimiento de usuarios con iPhone para visualizarlos en un modelo 3D

El objetivo de este proyecto es desarrollar un sistema de seguimiento de usuarios con un iPhone y un modelo 3D del campus de la Technical University of Denmark. El usuario podrá activar el seguimiento tras abrir una aplicación en el iPhone siempre y cuando se encuentre en alguna de las áreas donde haya un modelo 3D disponible. Los usuarios que hayan activado el seguimiento serán mostrados en estos modelos 3D en forma de avatares. Los modelos 3D junto con los avatares pueden ser visualizados usando cualquier navegador de escritorio en la página web realsite.dk. Los sensores GPS de los Smartphones no son normalmente muy precisos. Para desarrollar buenos algoritmos en el sistema de seguimiento requerido, la precisión de este sensor tiene que ser analizada. Por esta razón el proyecto empieza con un extenso estudio de la precisión de los sistemas de localización en el iPhone y de los parámetros que pueden configurarse. Se estudian tanto posiciones fijas como en movimiento. Este estudio revela que el error medio en posiciones estáticas es en torno a 8 metros y bastante mayor para las posiciones en movimiento. Sin embargo es muy rápido determinando la primera posición lo cual lo hace en menos de 10 segundos en la mayoría de los casos. Utilizando los resultados de este estudio, se han diseñado varios filtros para eliminar las posiciones menos precisas. Además, también se ha desarrollado una técnica que permite detectar cuando el usuario entra dentro de un edificio sin usar ninguna información adicional más que la que los servicios de localización ofrecen. Las dos partes mas importantes de este sistema han sido desarrolladas en su totalidad en este proyecto fin de carrera. Estas son una aplicación para el sistema operativo móvil iOS y un algoritmo para representar a los avatares de los usuarios en los modelos 3D. La aplicación recoge las posiciones de los usuarios, utilizando el GPS del dispositivo, las filtra, las guarda y las manda a un servidor de internet donde son almacenadas en una base de datos. También permite visualizar las sesiones anteriores en las que el seguimiento ha sido activado y tomar una foto que será utilizada en el avatar del usuario. La representación de los avatares en el modelo no se puede llevar a cabo con las posiciones que el dispositivo iOS obtiene ya que no son suficientemente precisas. Por lo que se diseñó un algoritmo que genera a partir de las posiciones GPS recibidas una ruta realista, factible y libre de obstáculos en el modelo. Un detalle importante por ejemplo, es que hace que los avatares utilicen escaleras y puertas de edificios cuando se detecta que han cambiado de altitud o entrado a un edificio respectivamente

Biclique communities

We present a novel method for detecting communities in bipartite networks. Based on an extension of the $k$-clique community detection algorithm, we demonstrate how modular structure in bipartite networks presents itself as overlapping bicliques. If bipartite information is available, the bi-clique community detection algorithm retains all of the advantages of the $k$-clique algorithm, but avoids discarding important structural information when performing a one-mode projection of the network. Further, the bi-clique community detection algorithm provides a new level of flexibility by incorporating independent clique thresholds for each of the non-overlapping node sets in the bipartite network.Comment: 10 pages, 6 figure

Monitoring Public Behavior During a Pandemic Using Surveys: Proof-of-Concept Via Epidemic Modelling

Implementing a lockdown for disease mitigation is a balancing act: Non-pharmaceutical interventions can reduce disease transmission significantly, but interventions also have considerable societal costs. Therefore, decision-makers need near real-time information to calibrate the level of restrictions. We fielded daily surveys in Denmark during the second wave of the COVID-19 pandemic to monitor public response to the announced lockdown. A key question asked respondents to state their number of close contacts within the past 24 hours. Here, we establish a link between survey data, mobility data, and, hospitalizations via epidemic modeling. Using Bayesian analysis, we then evaluate the usefulness of survey responses as a tool to monitor the effects of lockdown and then compare the predictive performance to that of mobility data. We find that, unlike mobility, self-reported contacts track the immediate behavioral response after the lockdown's announcement, weeks before the lockdown's national implementation. The survey data agree with the inferred effective reproduction number and their addition to the model results in greater improvement of predictive performance than mobility data. A detailed analysis of contact types indicates that disease transmission is driven by friends and strangers, whereas contacts to colleagues and family members (outside the household) only played a minor role despite Christmas holidays. Our work shows that an announcement of non-pharmaceutical interventions can lead to immediate behavioral responses, weeks before the actual implementation. Specifically, we find that self-reported contacts capture this early signal and thus qualify as a reliable, non-privacy invasive monitoring tool to track the implementation of non-pharmaceutical interventions

Evidence of complex contagion of information in social media: An experiment using Twitter bots

It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using 'social bots' deployed to carry out coordinated attempts at spreading information. We propose two Bayesian statistical models describing simple and complex contagion dynamics, and test the competing hypotheses. We provide experimental evidence that the complex contagion model describes the observed information diffusion behavior more accurately than simple contagion. Future applications of our results include more effective defenses against malicious propaganda campaigns on social media, improved marketing and advertisement strategies, and design of effective network intervention techniques