Matteo Zignani

On the bursty evolution of online social networks

The high level of dynamics in todays online social networks (OSNs) creates new challenges for their infrastructures and providers. In particular, dynamics involving edge creation has direct implications on strategies for resource allocation, data partitioning and replication. Understanding network dynamics in the context of physical time is a critical first step towards a predictive approach towards infrastructure management in OSNs. Despite increasing efforts to study social network dynamics, current analyses mainly focus on change over time of static metrics computed on snapshots of social graphs. The limited prior work models network dynamics with respect to a logical clock. In this paper, we present results of analyzing a large timestamped dataset describing the initial growth and evolution of a large social network in China. We analyze and model the burstiness of link creation process, using the second derivative, i.e. the acceleration of the degree. This allows us to detect bursts, and to characterize the social activity of a OSN user as one of four phases: acceleration at the beginning of an activity burst, where link creation rate is increasing; deceleration when burst is ending and link creation process is slowing; cruising, when node activity is in a steady state, and complete inactivity.

Extracting human mobility patterns from GPS-based traces

In this paper we analyze few GPS-based traces to infer human mobility patterns. We propose a clustering method to extract the main points of interest, called geo-locations, from GPS data. Starting from geo-locations we propose a definition of community, the geo-community, which captures the relation between a spatial description of human movements and the social context where users live. A statistical analysis of the principal characteristics of human walks provide the fitting distributions of distances covered by people inside a geo-location and among geo-locations and pause time. Finally we analyze factors influencing people when choosing successive location in their movement.

How many places do you visit a day

People mobility enormously augmented in the last decades. However, despite the increased possibilities of fast reaching far places, the places that a person commonly visits remain limited in number. The number of visited places of each person is regulated by some laws that are statistically similar among individuals. In our previous work, we firstly argued that a person visit most frequently always few places, and we confirmed that by some initial experiments. Here, in addition to further validating this result, we build a more sophisticate view of the places visited by the people. Namely, on top of our previous work, which identifies the class of Mostly Visited Points of Interest, we define two next classes: the Occasionally and the Exceptionally Visited Points of Interest classes. We argue and validate on real data, that also the occasional places are very limited in number, while the exceptional ones can grow at will, and by the analysis of the classes of visited points we can distinguish the type of users mobility. This paper firstly demonstrates this property in large experimental scenario, and put the basis for new understanding of people places in several areas as localization, social interactions and human mobility modelling.

Extracting human mobility and social behavior from location‐aware traces

The concepts of location and community are rapidly becoming key points in the design of new communication paradigms and in deploying emerging mobile computing services. The need of reliable and quantitative knowledge and predictions of some relevant information, such as which locations are enjoyed by people in their daily lives and how people aggregate within communities, advocates a realistic mobility model able to describe both the human mobility throughout locations and the human attitude to socialize within communities. Unfortunately, so far, neither the concept of location nor the concept of community has been univocally defined. In this paper, we approach the problem from the most basic of starting points, namely by analyzing the real Global Positioning System datasets of human mobility traces. On this elementary basis, the paper provides a few relevant contributions. We firstly derive a deep understanding of the term “location” and at the same time of the notion of community strictly related to it. Secondly, we merge the two concepts into what we call geo-community. By proceeding from real spatial data rather than from a priori reasonings, we are able to quantitatively describe geo-communities and infer the probability distributions of all the features of human behavior. Finally, not to lose social implications, we present the method to derive people sociality from geo-communities. Copyright

On the properties of human mobility

Visited locations are classified in 3 main categories according to their relevance.People visit regularly just few places where they spend most of their time.People commute between places based on their time (not spatial) distance.HOME and WORK places are in the set of few places mostly visited.Mostly visited places semantic inference is based on user mobility/behavior analysis. 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 peoples 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.

Multidimensional human dynamics in mobile phone communications

In todays technology-assisted society, social interactions may be expressed through a variety of techno-communication channels, including online social networks, email and mobile phones (calls, text messages). Consequently, a clear grasp of human behavior through the diverse communication media is considered a key factor in understanding the formation of the todays information society. So far, all previous research on user communication behavior has focused on a sole communication activity. In this paper we move forward another step on this research path by performing a multidimensional study of human sociality as an expression of the use of mobile phones. The paper focuses on user temporal communication behavior in the interplay between the two complementary communication media, text messages and phone calls, that represent the bi-dimensional scenario of analysis. Our study provides a theoretical framework for analyzing multidimensional bursts as the most general burst category, that includes one-dimensional bursts as the simplest case, and offers empirical evidence of their nature by following the combined phone call/text message communication patterns of approximately one million people over three-month period. This quantitative approach enables the design of a generative model rooted in the three most significant features of the multidimensional burst - the number of dimensions, prevalence and interleaving degree - able to reproduce the main media usage attitude. The other findings of the paper include a novel multidimensional burst detection algorithm and an insight analysis of the human media selection process.

Geo-CoMM: A geo-community based mobility model

The paper proposes a new mobility model able to properly reproduce the spatial, temporal and social features that can be observed in real mobility datasets. The model, named Geo-CoMM, is based on the quantities that guide human mobility and their probability distributions by directly extracting their setting from the statistical analysis of GPS-based traces. In Geo-CoMM, people move within a set of geo-communities, i.e. locations loosely shared among people, following speed, pause time and choice rules whose distribution is obtained by the statistical analysis; similarly, inside a geo-community, people move according to a Lévy walk. The paper also introduces a methodology to derive social relationships from traces, by representing the system (node, geocommunity) as a bipartite graph whose projections on nodes indicate the strength of the relationships amongst nodes. Finally, simulation results are presented to show how the model correctly reproduces all the statistics of some real trace datasets through a simple setting of environment parameters.

Simulating human mobility patterns in urban areas

Abstract With the rise of smart cities people are moving within urban spaces and still be able to pervasively interact with information, services, city’s resources and other people. In such a highly connected scenario, smartphones and other wireless portable devices are carried by humans, exhibit the same mobility behaviour of their human carriers and their movements strongly impact on the underlying network operation and performance. The understanding of human mobility in an urban space has become crucial to optimize the network management, to plan the adaptive allocation of critical resources and ensure constant quality of the user experience. This paper takes a first step in the direction of the design of a mobility model meeting behavioural and scale requirements of modern smart cities. We envision a smart city as a collection of places, each representing a Point of Interest (PoI) with specific value for single individuals and for a set of them. As a consequence, each individual has his/her own mobility footprint, while few of them share similar mobility patterns. By simulating the mobility of each individual across city’s places, we will be able to properly describe human mobility and social behaviour in urban spaces, and to extract all needed information about how city’s resources and services are accessed. The extensive use of CDR, GPS and WiFi traces, enables us to analyse the characteristics of citys Points of Interest (PoI), classify them for each individual according to their importance and study how the individuals move across them. The common features observed are the key points to build a metropolitan mobility simulator able to reproduce the regularity in spatio-temporal behaviour of mobile users and also how city sociality is built around PoIs of the city. The simulator exhibits high flexibility and can be applied in wide geographical and population scales.

Human mobility model based on time-varying bipartite graph

Nowadays human beings are surrounded by a heterogeneous networking environment consisting a growing number of portable computation and communication devices. As most devices are carried out by human beings, such a contact-based networks is highly influenced by human mobility. This fact implies that the presence of possible patterns in human movements can be exploited by wireless network applications in order to extract sensible informations on top of which novel mobile services can be deployed. Such information does not cover only the spatial or temporal dimension, but also concerns relational and social aspects of the involved people. In order to evaluate such applications we have to develop a mobility simulation sufficiently expressive and easily tunable. The most important goal in the mobility model research area is to provide a tool that can capture the most important and relevant features regarding both physical and social dimensions. For my PhD research I propose a new mobility model able to properly reproduce the spatial, temporal and social features that can be observed in real mobility datasets. In the model people move within a set of geo-communities, i.e. locations loosely shared among people, according to a bipartite time-varying graph; similarly, inside a geo-community, people move according to a modified version of classical random waypoint. We also derive social relationships from the bipartite graph representation by means of different types of projections on the node set. The purpose of this document is to briefly describe the state of the art in mobility model, and to outline my planned PhD research.

Urban communications and social interactions through the lens of mobile phone data

Abstract The social network built on top of mobile phone data has drawn increasing attention in recent years, due to its being far more accurate than its online counterpart in mirroring people’s offline sociality. In this paper, we leverage a large dataset of mobile Call Detail Records (CDRs), provided by one of the primary Italian operators, to build the multiplex social network given by call and text message activities of the operator’s subscribers. By discussing the multiplex network characteristics – ranging from ego-networks, dyads and triads all the way to cliques – the paper offers a comprehensive and thorough overview of human sociality as carried on through mobile phone; in addition, it highlights the need to consider more than one communication media when aiming to understand people’s sociality.Finally, by investigating on-phone cliques, we show people’s inclination to gather in cohesive and restricted groups of close friends, thus providing strong ominous indicators of where many recent online social networks, namely Snapchat, WeChat and others, are leading.