1,721,027 research outputs found
Human mobility model based on time-varying bipartite graph
No full textNowadays 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
Geo-CoMM: A geo-community based mobility model
No full textThe 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
FROM SMALL-WORLDS TO BIG DATA:TEMPORAL AND MULTIDIMENSIONAL ASPECTS OF HUMAN NETWORKS
Full text linkIn this thesis we address the close interplay among mobility, offline relationships and online interactions and the related human networks at different dimensional scales and temporal granularities. By generally adopting a data-driven approach, we move from small datasets about physical interactions mediated by human-carried devices, describing small social realities, to large-scale graphs that evolve over time, as well as from human mobility trajectories to face-to-face contacts occurring in different geographical contexts.
We explore in depth the relation between human mobility and the social structure induced by the overlapping of different people's trajectories on GPS traces collected in urban and metropolitan areas. We define the notions of geo-location and geo-community which are operational in describing in a unique framework both spatial and social aspects of human behavior. Through the concept of geo-community we model the human mobility adopting a bipartite graph. Thanks to this graph representation we can generate a social structure that is plausible w.r.t. the real interactions. In general the modeling approach have the merit for reporting the mobility in a graph-theoretic framework making the study of the interplay mobility/sociality more affordable and intuitive.
Our modeling approach also results in a mobility model, Geo-CoMM, which lies on and exploits the idea of geo-community. The model represents a particular instance of a general framework we provide. A framework where the social structure behind the preferred-location based mobility models emerges. We validate Geo-CoMM on spatial, temporal, pairwise connectivity and social features showing that it reproduces the main statistical properties observed in real traces.
As concerns the offline/online interplay we provide a complete overview of the close connection between online and offline sociality. To reach our goal we gather data about offline contacts and social interactions on Facebook of a group of students and we propose a multidimensional network analysis which allows us to deeply understand how the characteristics of users in the distinct networks impact each other. Results show how offline and Facebook friends are different. This way we confirm and worsen the general intuition that online social networks have shifted away from their original goal to mirror the offline sociality of individuals. As for the role and the social importance, it becomes apparent that social features such as user popularity or community structure do not transfer along social dimensions, as confirmed by our correlation analysis of the network layers and by the comparison among the communities.
In the last chapters we analyze the evolution of the online social network from a physical time perspective, i.e. considering the graph evolution as a graph time-series and not as a function of the network basic properties (number of nodes or links).
As for the physical time in a user-centric viewpoint, we investigate the bursty nature of the link creation process in online social network. We prove not only that it is a highly inhomogeneous process, but also identify patterns of burstiness common to all nodes. Then we focus on the dynamic formation of two fundamental network building components: dyads and triads. We propose two new metrics to aid the temporal analysis on physical time: link creation delay and triangle closure delay. These two metrics enable us to study the dynamic creation of dyads and triads, and to highlight network behavior that would otherwise remain hidden. In our analysis, we find that link delays are generally very low in absolute time and are largely independent of the dates people join the network. To highlight the social nature of this metric, we introduce the term \textit{peerness} to quantify how well linked users overlap in lifetimes. As for triadic closure delay we first introduce an algorithm to extract of temporal triangle which enables us to monitor the triangle formation process, and to detect sudden changes in the triangle formation behavior, possibly related to external events. In particular, we show that the introduction of new service functionalities had a disruptive impact on the triangle creation process in the network
Extracting human mobility and social behavior from location-aware traces
No full textThe 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
Extracting human mobility patterns from GPS-based traces
No full textIn 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
Calling and texting: social interactions in a multidimensional telecom graph
No full textThe growing awareness that human communications and social interactions are assuming a stratified structure, due to the availability of multiple techno-communication channels, including online social networks, mobile phone calls, short messages (SMS) and e-mails, has recently led to the study of multidimensional networks. In this context we perform the first study of the multiplex mobile social network, gathered from the records of both call and text message activities of millions of users of a large mobile phone operator over a period of 12 weeks. While social networks constructed from mobile phone datasets have drawn great attention in recent years, so far studies have dealt with text message and call data, separately, providing a very partial view of people sociality expressed on phone. Here we analyze how the call and the text message dimensions overlap showing how many information about links and nodes could be lost only accounting for a single layer and how users adopt different media channels to interact with their neighborhood
Predicting the link strength of "newborn" links
No full textMeasurements of online social networks (OSNs) support the common fact that not all links carry the same social value, and that the strength of each link is strictly related to the frequency of interactions between the connected users. In this paper, we investigate the predictability of the interactions on OSN links by wondering if it is possible to categorize interactive or non-interactive links at their creation time. We turn the problem into a binary classification task and introduce a set of features which leverage the temporal and topological properties of the social and interaction networks, without requiring the knowledge of the interaction history of the link. The best classifier trained on a Facebook dataset obtained 0.72 as AUC. The above performance suggests that we can distinguish between interactive/non-interactive links at the time of link creation
User identification across online social networks in practice : Pitfalls and solutions
No full textTo take advantage of the full range of services that online social networks (OSNs) offer, people commonly open several accounts on diverse OSNs where they leave lots of different types of profile information. The integration of these pieces of information from various sources can be achieved by identifying individuals across social networks. In this article, we address the problem of user identification by treating it as a classification task. Relying on common public attributes available through the official application programming interface (API) of social networks, we propose different methods for building negative instances that go beyond usual random selection so as to investigate the effectiveness of each method in training the classifier. Two test sets with different levels of discrimination are set up to evaluate the robustness of our different classifiers. The effectiveness of the approach is measured in real conditions by matching profiles gathered from Google+, Facebook and Twitter
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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