10 research outputs found
Social Events in a Time-Varying Mobile Phone Graph
The large-scale study of human mobility has been significantly enhanced over the last decade by the massive use of mobile phones in urban populations. Studying the activity of mobile phones allows us, not only to infer social networks between individuals, but also to observe the movements of these individuals in space and time. In this work, we investigate how these two related sources of information can be integrated within the context of detecting and analyzing large social events. We show that large social events can be characterized not only by an anomalous increase in activity of the antennas in the neighborhood of the event, but also by an increase in social relationships of the attendants present in the event. Moreover, having detected a large social event via increased antenna activity, we can use the network connections to infer whether an unobserved user was present at the event. More precisely, we address the following three challenges: (i) automatically detecting large social events via increased antenna activity; (ii) characterizing the social cohesion of the detected event; and (iii) analyzing the feasibility of inferring whether unobserved users were in the event.Sociedad Argentina de Informática e Investigación Operativa (SADIO
Social Events in a Time-Varying Mobile Phone Graph
The large-scale study of human mobility has been significantly enhanced over the last decade by the massive use of mobile phones in urban populations. Studying the activity of mobile phones allows us, not only to infer social networks between individuals, but also to observe the movements of these individuals in space and time. In this work, we investigate how these two related sources of information can be integrated within the context of detecting and analyzing large social events. We show that large social events can be characterized not only by an anomalous increase in activity of the antennas in the neighborhood of the event, but also by an increase in social relationships of the attendants present in the event. Moreover, having detected a large social event via increased antenna activity, we can use the network connections to infer whether an unobserved user was present at the event. More precisely, we address the following three challenges: (i) automatically detecting large social events via increased antenna activity; (ii) characterizing the social cohesion of the detected event; and (iii) analyzing the feasibility of inferring whether unobserved users were in the event.Sociedad Argentina de Informática e Investigación Operativa (SADIO
Social Events in a Time-Varying Mobile Phone Graph
The large-scale study of human mobility has been significantly enhanced over the last decade by the massive use of mobile phones in urban populations. Studying the activity of mobile phones allows us, not only to infer social networks between individuals, but also to observe the movements of these individuals in space and time. In this work, we investigate how these two related sources of information can be integrated within the context of detecting and analyzing large social events. We show that large social events can be characterized not only by an anomalous increase in activity of the antennas in the neighborhood of the event, but also by an increase in social relationships of the attendants present in the event. Moreover, having detected a large social event via increased antenna activity, we can use the network connections to infer whether an unobserved user was present at the event. More precisely, we address the following three challenges: (i) automatically detecting large social events via increased antenna activity; (ii) characterizing the social cohesion of the detected event; and (iii) analyzing the feasibility of inferring whether unobserved users were in the event.Sociedad Argentina de Informática e Investigación Operativa (SADIO
Analyzing the Spread of Chagas Disease with Mobile Phone Data
We use mobile phone records for the analysis of mobility patterns and the
detection of possible risk zones of Chagas disease in two Latin American
countries. We show that geolocalized call records are rich in social and
individual information, which can be used to infer whether an individual has
lived in an endemic area. We present two case studies, in Argentina and in
Mexico, using data provided by mobile phone companies from each country. The
risk maps that we generate can be used by health campaign managers to target
specific areas and allocate resources more effectively.Comment: 6 pages, 6 figure
Uncovering the Spread of an Infectious Disease with Mobile Phone Data
We use mobile phone records for the analysis of mobility patterns and the detection of possible risk zones of Chagas disease in two Latin American countries. We show that geolocalized call records are rich in social and individual information, which can be used to infer whether an individual has lived in an endemic area. We present two case studies, in Argentina and in Mexico, using data provided by mobile phone companies from each country. The risk maps that we generate can be used by health campaign managers to target specific areas and allocate resources more effectively. Finally, we show the value of mobile phone records to predict long-term migrations, which play a crucial role in the spread of Chagas disease.Sociedad Argentina de Informática e Investigación Operativa (SADIO
Uncovering the Spread of an Infectious Disease with Mobile Phone Data
We use mobile phone records for the analysis of mobility patterns and the detection of possible risk zones of Chagas disease in two Latin American countries. We show that geolocalized call records are rich in social and individual information, which can be used to infer whether an individual has lived in an endemic area. We present two case studies, in Argentina and in Mexico, using data provided by mobile phone companies from each country. The risk maps that we generate can be used by health campaign managers to target specific areas and allocate resources more effectively. Finally, we show the value of mobile phone records to predict long-term migrations, which play a crucial role in the spread of Chagas disease.Sociedad Argentina de Informática e Investigación Operativa (SADIO
MultiAspect Graphs: Algebraic representation and algorithms
We present the algebraic representation and basic algorithms for MultiAspect
Graphs (MAGs). A MAG is a structure capable of representing multilayer and
time-varying networks, as well as higher-order networks, while also having the
property of being isomorphic to a directed graph. In particular, we show that,
as a consequence of the properties associated with the MAG structure, a MAG can
be represented in matrix form. Moreover, we also show that any possible MAG
function (algorithm) can be obtained from this matrix-based representation.
This is an important theoretical result since it paves the way for adapting
well-known graph algorithms for application in MAGs. We present a set of basic
MAG algorithms, constructed from well-known graph algorithms, such as degree
computing, Breadth First Search (BFS), and Depth First Search (DFS). These
algorithms adapted to the MAG context can be used as primitives for building
other more sophisticated MAG algorithms. Therefore, such examples can be seen
as guidelines on how to properly derive MAG algorithms from basic algorithms on
directed graph. We also make available Python implementations of all the
algorithms presented in this paper.Comment: 59 pages, 6 figure
Uncovering the Spread of an Infectious Disease with Mobile Phone Data
We use mobile phone records for the analysis of mobility patterns and the detection of possible risk zones of Chagas disease in two Latin American countries. We show that geolocalized call records are rich in social and individual information, which can be used to infer whether an individual has lived in an endemic area. We present two case studies, in Argentina and in Mexico, using data provided by mobile phone companies from each country. The risk maps that we generate can be used by health campaign managers to target specific areas and allocate resources more effectively. Finally, we show the value of mobile phone records to predict long-term migrations, which play a crucial role in the spread of Chagas disease.Sociedad Argentina de Informática e Investigación Operativa (SADIO