The elliptic model for communication fluxes

In this paper, a model (called the elliptic model) is proposed to estimate the number of social ties between two locations using population data in a similar manner to how transportation research deals with trips. To overcome the asymmetry of transportation models, the new model considers that the number of relationships between two locations is inversely proportional to the population in the ellipse whose foci are in these two locations. The elliptic model is evaluated by considering the anonymous communications patterns of 25 million users from three different countries, where a location has been assigned to each user based on their most used phone tower or billing zip code. With this information, spatial social networks are built at three levels of resolution: tower, city and region for each of the three countries. The elliptic model achieves a similar performance when predicting communication fluxes as transportation models do when predicting trips. This shows that human relationships are influenced at least as much by geography as is human mobility

Epidemic processes in complex networks

In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio

An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System

Incorporating speed probability distribution to the computation of the route planning in car navigation systems guarantees more accurate and precise responses. In this paper, we propose a novel approach for dynamically selecting the number of samples used for the Monte Carlo simulation to solve the Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the computation efficiency. The proposed method is used to determine in a proactive manner the number of simulations to be done to extract the travel-time estimation for each specific request while respecting an error threshold as output quality level. The methodology requires a reduced effort on the application development side. We adopted an aspect-oriented programming language (LARA) together with a flexible dynamic autotuning library (mARGOt) respectively to instrument the code and to take tuning decisions on the number of samples improving the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36% and 81%) with respect to a static approach while considering different traffic situations, paths and error requirements. Given the negligible runtime overhead of the proposed approach, it results in an execution-time speedup between 1.5x and 5.1x. This speedup is reflected at infrastructure-level in terms of a reduction of around 36% of the computing resources needed to support the whole navigation pipeline

Three Essays on Network Dynamics and Liminality

This dissertation focuses on the emergence and evolution of social networks by paying particular attention to the spanning of cultural boundaries that segregate actors in the context of specific societies. In particular, I use systems science methods to study the bridging of cultural holes in small and relatively dense artificial societies, as well as in an American high school. I also study the significance of local triadic configurations in giving rise to the highly hierarchical system of aggregate-level migration flows in place in the Americas during the late 20th century. I use the concept of liminality as a way to analyze these disparate social systems. More precisely, I focus on the role of cultural brokers seen as actors at the limen – i.e. at the border – of symbolic boundaries, actors that can act as bridges between culturally disconnected worlds. In this context, this dissertation explains key network dynamics behind two emergent phenomena that are the direct result of liminal agents’ behaviors: the diffusion of innovations (Chapters 1 and 2) and a system of international migration flows (Chapter 3). Finally, I also put forward a critical view on brokerage based on different cases mentioned in the literature (e.g. 1.5 generation migrants or multiracial individuals) that show how the spanning of cultural holes can put brokers at an increased risk of being socially and/or psychologically harmed

Text-based document geolocation and its application to the digital humanities

This dissertation investigates automatic geolocation of documents (i.e. identification of their location, expressed as latitude/longitude coordinates), based on the text of those documents rather than metadata. I assert that such geolocation can be performed using text alone, at a sufficient accuracy for use in real-world applications. Although in some corpora metadata is found in abundance (e.g. home location, time zone, friends, followers, etc. in Twitter), it is lacking in others, such as many corpora of primary-source documents in the digital humanities, an area to which document geolocation has hardly been applied. To this end, I first develop methods for accurate text-based geolocation and then apply them to newly-annotated corpora in the digital humanities. The geolocation methods I develop use both uniform and adaptive (k-d tree) grids over the Earth’s surface, culminating in a hierarchical logistic-regression-based technique that achieves state of the art results on well-known corpora (Twitter user feeds, Wikipedia articles and Flickr image tags). In the second part of the dissertation I develop a new NLP task, text-based geolocation of historical corpora. Because there are no existing corpora to test on, I create and annotate two new corpora of significantly different natures (a 19th-century travel log and a large set of Civil War archives). I show how my methods produce good geolocation accuracy even given the relatively small amount of annotated data available, which can be further improved using domain adaptation. I then use the predictions on the much larger unannotated portion of the Civil War archives to generate and analyze geographic topic models, showing how they can be mined to produce interesting revelations concerning various Civil War-related subjects. Finally, I develop a new geolocation technique for text-only corpora involving co-training between document-geolocation and toponym- resolution models, using a gazetteer to inject additional information into the training process. To evaluate this technique I develop a new metric, the closest toponym error distance, on which I show improvements compared with a baseline geolocator.Linguistic

Spatial spread of farm animal diseases

Data on cattle movements within the United Kingdom have recently become available. As part of the conditions for lifting an export ban on British beef following the bovine spongiform encephalopathy epidemic, the European Union required that the UK should have "An effective animal identification and movement recording system". The Cattle Tracing System (CTS) was introduced in September 1998, and the scheme was extended to include all cattle by the beginning of 2001. Contact networks have proved valuable in studying the epidemiology of diseases in man, such as human immunodeficiency virus; the availability of CTS cattle movement data has enabled contact network analysis to be applied to diseases of farm livestock. The CTS data may be represented as a large network; cattle holdings are represented as nodes, with a movement of cattle between holdings being an edge. To address concerns about the quality of this cattle movement data, a field study was conducted on Lewis, one of the Western Isles of Scotland. Farmers were recruited with the assistance of the local veterinary surgeon, and asked to record a range of potential risk behaviours relating to the transmission of infectious diseases (moving livestock, sharing pasture, etc.) for a one-month period. For the study area in question, movements of cattle not reported to CTS (especially to or from common grazing land) were a substantial contribution to the contact network during the study period. A wide range of measures of network structure exist, but their relevance to the dynamics of infectious diseases on networks is unclear. To address this, a discrete-time stochastic SIR simulation model of disease on a network was designed and implemented in software. Using this simulation model, a network model with the key structural features of the CTS contact network was constructed, by considering a range of measures of network structure, and testing resulting model networks against CTS-derived networks. The resulting model was shown to predict the dynamics of a simulated disease model on that contact network more closely than existing models of global network structure. Much work on the contact structure of the UK cattle herd has relied on relatively simple static network representations of movement data. By using simulated diseases, the serious shortcomings of static network representations compared to more complex dynamic network representations were demonstrated. A substantial library of software for the generation and analysis of large networks, and the simulation of disease thereupon, has been produced, and has been made generally available. The design and implementation of this software is discussed, including the algorithms and data structures deployed, as well as validation of the software, and its portability to different computing platforms.This work was funded by BBSRC and the Tetra-Laval Research Fund; its revision was funded by the Wellcome Trust

Group Testing with Side Information via Generalized Approximate Message Passing

Group testing can help maintain a widespread testing program using fewer resources amid a pandemic. In a group testing setup, we are given n samples, one per individual. Each individual is either infected or uninfected. These samples are arranged into m < n pooled samples, where each pool is obtained by mixing a subset of the n individual samples. Infected individuals are then identified using a group testing algorithm. In this paper, we incorporate side information (SI) collected from contact tracing (CT) into nonadaptive/single-stage group testing algorithms. We generate different types of possible CT SI data by incorporating different possible characteristics of the spread of the disease. These data are fed into a group testing framework based on generalized approximate message passing (GAMP). Numerical results show that our GAMP-based algorithms provide improved accuracy. Compared to a loopy belief propagation algorithm, our proposed framework can increase the success probability by 0.25 for a group testing problem of n = 500 individuals with m = 100 pooled samples.Comment: arXiv admin note: substantial text overlap with arXiv:2106.02699, arXiv:2011.1418

A Survey on Centrality Metrics and Their Implications in Network Resilience

Centrality metrics have been used in various networks, such as communication, social, biological, geographic, or contact networks. In particular, they have been used in order to study and analyze targeted attack behaviors and investigated their effect on network resilience. Although a rich volume of centrality metrics has been developed for decades, a limited set of centrality metrics have been commonly in use. This paper aims to introduce various existing centrality metrics and discuss their applicabilities and performance based on the results obtained from extensive simulation experiments to encourage their use in solving various computing and engineering problems in networks.Comment: Main paper: 36 pages, 2 figures. Appendix 23 pages,45 figure