Fundamental structures of dynamic social networks

Social systems are in a constant state of flux with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding spreading of influence or diseases, formation of friendships, and the productivity of teams. While there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the micro-dynamics of social networks. Here we explore the dynamic social network of a densely-connected population of approximately 1000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geo-location, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-minute time slices we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores are preceded by coordination behavior in the communication networks, and demonstrating that social behavior can be predicted with high precision.Comment: Main Manuscript: 16 pages, 4 figures. Supplementary Information: 39 pages, 34 figure

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

Unraveling pedestrian mobility on a road network using ICTs data during great tourist events

Tourist flows in historical cities are continuously growing in a globalized world and adequate governance processes, politics and tools are necessary in order to reduce impacts on the urban livability and to guarantee the preservation of cultural heritage. The ICTs offer the possibility of collecting large amount of data that can point out and quantify some statistical and dynamic properties of human mobility emerging from the individual behavior and referring to a whole road network. In this paper we analyze a new dataset that has been collected by the Italian mobile phone company TIM, which contains the GPS positions of a relevant sample of mobile devices when they actively connected to the cell phone network. Our aim is to propose innovative tools allowing to study properties of pedestrian mobility on the whole road network. Venice is a paradigmatic example for the impact of tourist flows on the resident life quality and on the preservation of cultural heritage. The GPS data provide anonymized georeferenced information on the displacements of the devices. After a filtering procedure, we develop specific algorithms able to reconstruct the daily mobility paths on the whole Venice road network. The statistical analysis of the mobility paths suggests the existence of a travel time budget for the mobility and points out the role of the rest times in the empirical relation between the mobility time and the corresponding path length. We succeed to highlight two connected mobility subnetworks extracted from the whole road network, that are able to explain the majority of the observed mobility. Our approach shows the existence of characteristic mobility paths in Venice for the tourists and for the residents. Moreover the data analysis highlights the different mobility features of the considered case studies and it allows to detect the mobility paths associated to different points of interest. Finally we have disaggregated the Italian and foreigner categories to study their different mobility behaviors

An inherent limiting factor of human mobility prediction

Predicting how we humans move within space and time is becoming a central topic in many scientific domains, ranging from epidemic propagation, urban planning to ride-sharing. However, current works neglect individuals' preferences for exploration and discovery of new places. Yet, noveltyseeking activities appear to have significant consequences on the ability to understand and predict individuals' trajectories. In this work, we propose a new approach for the identification of moments of novelty-seeking. Subsequently, we construct individuals' mobility profiles based on their exploration inclinations-Scouters (i.e., extreme explorers), Routiners (i.e., extreme returners), and Regulars (i.e., without extreme behavior)

The impact of human mobility data scales and processing on movement predictability

Predictability of human movement is a theoretical upper bound for the accuracy of movement prediction models, which serves as a reference value showing how regular a dataset is and to what extent mobility can be predicted. Over the years, the predictability of various human mobility datasets was found to vary when estimated for differently processed datasets. Although attempts at the explanation of this variability have been made, the extent of these experiments was limited. In this study, we use high-precision movement trajectories of individuals to analyse how the way we represent the movement impacts its predictability and thus, the outcomes of analyses made on these data. We adopt a number of methods used in the last 11 years of research on human mobility and apply them to a wide range of spatio-temporal data scales, thoroughly analysing changes in predictability and produced data. We find that spatio-temporal resolution and data processing methods have a large impact on the predictability as well as geometrical and numerical properties of human mobility data, and we present their nonlinear dependencies

On the properties of human mobility

The current age of increased people mobility calls for a better understanding of how people move: how many places does an individual commonly visit, what are the semantics of these places, and how do people get from one place to another. We show that the number of places visited by each person (Points of Interest - PoIs) is regulated by some properties that are statistically similar among individuals. Subsequently, we present a PoIs classification in terms of their relevance on a per-user basis. In addition to the PoIs relevance, we also investigate the variables that describe the travel rules among PoIs in particular, the spatial and temporal distance. As regards the latter, existing works on mobility are mainly based on spatial distance. Here we argue, rather, that for human mobility the temporal distance and the PoIs relevance are the major driving factors. Moreover, we study the semantic of PoIs. This is useful for deriving statistics on people's habits without breaking their privacy. With the support of different datasets, our paper provides an in-depth analysis of PoIs distribution and semantics; it also shows that our results hold independently of the nature of the dataset in use. We illustrate that our approach is able to effectively extract a rich set of features describing human mobility and we argue that this can be seminal to novel mobility research

A Probabilistic Kernel Method for Human Mobility Prediction with Smartphones

Human mobility prediction is an important problem which has a large num- ber of applications, especially in context-aware services. This paper presents a study on location prediction using smartphone data, in which we address mod- eling and application aspects. Building personalized location prediction models from smartphone data remains a technical challenge due to data sparsity, which comes from the complexity of human behavior and the typically limited amount of data available for individual users. To address this problem, we propose an approach based on kernel density estimation, a popular smoothing technique for sparse data. Our approach contributes to existing work in two ways. First, our proposed model can estimate the probability that a user will be at a given location at a specific time in the future, by using both spatial and temporal information via multiple kernel functions. Second, we also show how our prob- abilistic framework extends to a more practical task of location prediction for a time window in the future. Our approach is validated on an everyday life location datasets consisting of 133 smartphone users. Our method reaches an accuracy of 84% for the next hour, and an accuracy of 77% for the next three hours