Google+ or Google-?: Dissecting the Evolution of the New OSN in its First Year

In the era when Facebook and Twitter dominate the market for social media, Google has introduced Google+ (G+) and reported a significant growth in its size while others called it a ghost town. This begs the question that "whether G+ can really attract a significant number of connected and active users despite the dominance of Facebook and Twitter?". This paper tackles the above question by presenting a detailed characterization of G+ based on large scale measurements. We identify the main components of G+ structure, characterize the key features of their users and their evolution over time. We then conduct detailed analysis on the evolution of connectivity and activity among users in the largest connected component (LCC) of G+ structure, and compare their characteristics with other major OSNs. We show that despite the dramatic growth in the size of G+, the relative size of LCC has been decreasing and its connectivity has become less clustered. While the aggregate user activity has gradually increased, only a very small fraction of users exhibit any type of activity. To our knowledge, our study offers the most comprehensive characterization of G+ based on the largest collected data sets.Comment: WWW 201

Reciprocity in Social Networks with Capacity Constraints

Directed links -- representing asymmetric social ties or interactions (e.g., "follower-followee") -- arise naturally in many social networks and other complex networks, giving rise to directed graphs (or digraphs) as basic topological models for these networks. Reciprocity, defined for a digraph as the percentage of edges with a reciprocal edge, is a key metric that has been used in the literature to compare different directed networks and provide "hints" about their structural properties: for example, are reciprocal edges generated randomly by chance or are there other processes driving their generation? In this paper we study the problem of maximizing achievable reciprocity for an ensemble of digraphs with the same prescribed in- and out-degree sequences. We show that the maximum reciprocity hinges crucially on the in- and out-degree sequences, which may be intuitively interpreted as constraints on some "social capacities" of nodes and impose fundamental limits on achievable reciprocity. We show that it is NP-complete to decide the achievability of a simple upper bound on maximum reciprocity, and provide conditions for achieving it. We demonstrate that many real networks exhibit reciprocities surprisingly close to the upper bound, which implies that users in these social networks are in a sense more "social" than suggested by the empirical reciprocity alone in that they are more willing to reciprocate, subject to their "social capacity" constraints. We find some surprising linear relationships between empirical reciprocity and the bound. We also show that a particular type of small network motifs that we call 3-paths are the major source of loss in reciprocity for real networks

A large-scale analysis of Facebook's user-base and user engagement growth

Understanding the evolution of the user base as well as the user engagement of online services is critical not only for the service operators but also for customers, investors, and users. While we can find research works addressing this issue in online services, such as Twitter, MySpace, or Google+, such detailed analysis is missing for Facebook, which is currently the largest online social network. This paper presents the first detailed study on the demographic and geographic composition and evolution of the user base and user engagement in Facebook over a period of three years. To this end, we have implemented a measurement methodology that leverages the marketing API of Facebook to retrieve actual information about the number of total users and the number of daily active users across 230 countries and age groups ranging between 13 and 65+. The conducted analysis reveals that Facebook is still growing and geographically expanding. Moreover, the growth pattern is heterogeneous across age groups, genders, and geographical regions. In particular, from a demography perspective, Facebook shows the lowest growth pattern among adolescents. Gender-based analysis showed that growth among men is still higher than the growth in women. Our geographical analysis reveals that while Facebook growth is slower in western countries, it has the fastest growth in the developing countries mainly located in Africa and Central Asia; analyzing the penetration of these countries also shows that these countries are at earlier stages of Facebook penetration. Leveraging external socioeconomic datasets, we also showed that this heterogeneous growth can be characterized by indicators, such as availability and access to Internet, Facebook popularity, and factors related with population growth and gender inequality.The work of Y. M. Kassa was supported by the European H2020 Project TYPES under Grant 653449. The work of R. Cuevas was supported in part by the European H2020 Project SMOOTH under Grant 786741, in part by the Spanish Ministry of Economy and Competitiveness, through the 5GCity Project, under Grant TEC2016-76795-C6-3-R, and in part by the La Caixa Foundation under Agreement LCF/PR/MIT17/11820009. The work of A. Cuevas was supported in part by the Ministerio de Economía, Industria y Competitividad, Spain, in part by the European Social Fund through the Ramón Y Cajal under Grant RyC-2015-17732, and in part by the Ministerio de Economía, Industria y Competitividad, Spain, through the Project TEXEO, under Grant TEC2016-80339-R.Publicad

Reciprocity in Social Networks with Capacity Constraints

ABSTRACT Directed links -representing asymmetric social ties or interactions (e.g., "follower-followee") -arise naturally in many social networks and other complex networks, giving rise to directed graphs (or digraphs) as basic topological models for these networks. Reciprocity, defined for a digraph as the percentage of edges with a reciprocal edge, is a key metric that has been used in the literature to compare different directed networks and provide "hints" about their structural properties: for example, are reciprocal edges generated randomly by chance or are there other processes driving their generation? In this paper we study the problem of maximizing achievable reciprocity for an ensemble of digraphs with the same prescribed in-and out-degree sequences. We show that the maximum reciprocity hinges crucially on the in-and outdegree sequences, which may be intuitively interpreted as constraints on some "social capacities" of nodes and impose fundamental limits on achievable reciprocity. We show that it is NP-complete to decide the achievability of a simple upper bound on maximum reciprocity, and provide conditions for achieving it. We demonstrate that many real networks exhibit reciprocities surprisingly close to the upper bound, which implies that users in these social networks are in a sense more "social" than suggested by the empirical reciprocity alone in that they are more willing to reciprocate, subject to their "social capacity" constraints. We find some surprising linear relationships between empirical reciprocity and the bound. We also show that a particular type of small network motifs that we call 3-paths are the major source of loss in reciprocity for real networks

Characterizing Online Social Media: Topic Inference and Information Propagation

Word-of-mouth (WOM) communication is a well studied phenomenon in the literature and content propagation in Online Social Networks (OSNs) is one of the forms of WOM mechanism that have been prevalent in recent years specially with the widespread surge of online communities and online social networks. The basic piece of information in most OSNs is a post (e.g., a tweet in Twitter or a post in Facebook). A post can contain different types of content such as text, photo, video, etc, or a mixture of two or more them. There are also various ways to enrich the text by mentioning other users, using hashtags, and adding URLs to external contents. The goal of this study is to investigate what factors contribute into the propagation of messages in Google+. To answer to this question a multidimensional study will be conducted. On one hand this question could be viewed as a natural language processing problem where topic or sentiment of posts cause message dissemination. On the other hand the propagation can be effect of graph properties i.e., popularity of message originators (node degree) or activities of communities. Other aspects of this problem are time, external contents, and external events. All of these factors are studied carefully to find the most highly correlated attribute(s) in the propagation of posts

MULTIDIMENSIONAL ANALYSIS OF PEOPLE'S BEHAVIOR IN ONLINE SOCIAL NETWORKS

L\u2019impressionante crescita in popolarit\ue0 delle Online Social Networks (OSNs), evidenziata dall\u2019enorme numero di utenti oggi legati ai social network pi\uf9 popolari, offre un\u2019opportunit\ue0 unica per comprendere i comportamenti online degli individui. In questa tesi, analizziamo i comportamenti delle persone sulle OSNs considerando che tali comportamenti sono il risultato della combinazione di esperienze ed attitudini sia online che offline. Dapprima, eseguiamo una analisi multidimensionale degli utenti attraverso diversi social media per fornire una descrizione complessiva dei comportamenti online e comprendere come questi cambino quando pi\uf9 media sono disponibili contemporaneamente. I risultati che presentiamo rappresentano uno dei primi esempi di esplorazione dei comportamenti umani su diversi social media. Ad esempio, utilizzando lo user degree su 5 diversi social network, evidenziamo che l\u2019importanza di ogni individuo cambia da piattaforma a piattaforma. La natura longitudinale del nostro dataset \ue8 anche stata sfruttata per studiare l\u2019attivit\ue0 di posting degli utenti, evidenziando una leggera correlazione positiva sulla frequenza con cui gli utenti pubblicano su social media differenti e confermando la natura bursty delle attivit\ue0 di posting mediante l\u2019uso di serie temporali multidimensionali. Inoltre, durante la tesi abbiamo sviluppato un metodo di identificazione innovativo per collegare le persone attraverso le diverse piattaforme social. Facendo riferimento agli attributi pubblici comuni, attraverso l\u2019uso di application programming interface (API) dei diversi social network, costruiamo le istanze negative in tre modi diversi, superando la selezione randomica abitualmente adottata, allo scopo di valutare la robustezza del nostro algoritmo di identificazione su diversi dataset. I risultati mostrano che l\u2019approccio porta ad un metodo di identificazione molto efficace per costruire dataset affidabili. Uno scenario reale costruito su Google+ e Facebook \ue8 stato utilizzato come testbed per la validazione del metodo. I risultati che riportiamo dimostrano i vantaggi ottenibili con il nuovo metodo rispetto ad altri metodi da letteratura. Infine, la tesi compie un primo passo verso una miglior comprensione degli effetti degli eventi offline sulla struttura del grafo delle social network in cui sono pubblicizzati. Pi\uf9 precisamente, svolgiamo una analisi temporale della social network legata all\u2019evento, comprendendo le persone che dichiarano di partecipare all\u2019evento tramite facebook, e valutiamo come questa evolva durante l\u2019intervallo temporale dell\u2019eventi stesso. I risultati mostrano che nuove amicizie nascono durante l\u2019evento e che la creazione di questi nuovi legami sociali \ue8 una delle cause principali di chiusura triangolare e che il grado maggiore si osserva durante l\u2019ultimo giorno dell\u2019evento stesso.The unprecedented and quickly increasing popularity of Online Social Networks (OSNs) is evidenced by the huge number of users who are turning to Facebook, Twitter and other social networks. The rapid growth of these online social networks provides a unique chance to study and understand the online behavior of the people. In this thesis, we analyze people's behavior in online social network considering the fact that online behavior of people is influenced by different factors which derive from the combination of their offline and online life. First, we perform a multidimensional analysis of users across multiple social media sites to give an all-around picture of people\u2019s online behavior. While people in their online life have access to a wide portfolio of social platforms, little is known about users\u2019 behavior when they have different online communication media available. Our findings represent some novel insights about people\u2019s behavior across social media. Having at our disposal users\u2019 degree in five different social networks, we find that the individuals\u2019 importance changes from medium to medium. The longitudinal nature of our dataset has been exploited to investigate the posting activity. We find a slightly positive correlation on how often users publish on different social media and we confirm the burstiness of the posting activities extending it to multidimensional time-series. Second, we develop an innovative identification methodology for connecting people across multiple social platforms. Relying on common public attributes available through the official application programming interface (API) of social networks, we construct negative instances in three different ways, going beyond the commonly adopted random selection to evaluate the robustness of our identification algorithm on different datasets. Results show that the approach can lead to a very effective identification method and methodology for building reliable datasets. Moreover, we analyzed the success of our method in a real scenario built on Google+/Facebook neighborhoods. Experiments reveal the advantages of the proposed method in comparison to previous methods in the literature. Finally, we take the first step towards understanding the effect of offline events on the graph structure of the social network where they are advertised. More precisely, we perform a temporal analysis of the event social network, constituted by people declaring to attend the event on Facebook and the links between them, and evaluated how it evolves during the event time period. The results show that new friendships are created during events and that this new friendships creation is one of the main reasons of triangle closure and the higher degrees observed in the last day of the events period

FROM SMALL-WORLDS TO BIG DATA:TEMPORAL AND MULTIDIMENSIONAL ASPECTS OF HUMAN NETWORKS

In 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

Data quality measures for identity resolution

The explosion in popularity of online social networks has led to increased interest in identity resolution from security practitioners. Being able to connect together the multiple online accounts of a user can be of use in verifying identity attributes and in tracking the activity of malicious users. At the same time, privacy researchers are exploring the same phenomenon with interest in identifying privacy risks caused by re-identification attacks. Existing literature has explored how particular components of an online identity may be used to connect profiles, but few if any studies have attempted to assess the comparative value of information attributes. In addition, few of the methods being reported are easily comparable, due to difficulties with obtaining and sharing ground- truth data. Attempts to gain a comprehensive understanding of the identifiability of profile attributes are hindered by these issues. With a focus on overcoming these hurdles to effective research, this thesis first develops a methodology for sampling ground-truth data from online social networks. Building on this with reference to both existing literature and samples of real profile data, this thesis describes and grounds a comprehensive matching schema of profile attributes. The work then defines data quality measures which are important for identity resolution, and measures the availability, consistency and uniqueness of the schema’s contents. The developed measurements are then applied in a feature selection scheme to reduce the impact of missing data issues common in identity resolution. Finally, this thesis addresses the purposes to which identity resolution may be applied, defining the further application-oriented data quality measurements of novelty, veracity and relevance, and demonstrating their calculation and application for a particular use case: evaluating the social engineering vulnerability of an organisation

Modelo de integración de la competencia digital docente en la enseñanza de la Matemática en la Universidad Tecnológica Equinoccial

La presente investigación tiene el propósito fundamental explorar el nivel de apropiación de la competencia digital del profesorado universitario del área de matemáticas en una universidad de Ecuador, considerando cuestiones relativas a la disponibilidad de infraestructuras, niveles de formación tecnológica y el grado de uso, integración e innovación de herramientas Web 2.0 en los procesos de enseñanza – aprendizaje de la matemática, como escenario para el crecimiento y fortalecimiento del ejercicio profesional docente. Para ello, el docente universitario necesita no solo técnicas y estrategias metodológicas, sino que debe ser capaz de integrar las herramientas tecnológicas (competencia digital docente) en su práctica educativa. La metodología de investigación utilizada es de tipo descriptivo con enfoque cuantitativo. Para la recogida de la información se diseñó el cuestionario M – CDUECDD, estructurado en cuatro dimensiones y 196 variables con las que se describe un perfil preliminar sobre competencias e indicadores que debe desarrollar el profesorado del área de matemáticas y que fue validado por jueces – expertos internacionales en competencias digitales. Los resultados muestran que el profesorado universitario del área de matemáticas tiene un nivel básico y medio sobre las cuestiones de dominio, uso e innovación en las cinco áreas: información y alfabetización informacional, comunicación y colaboración, creación de contenido digital, seguridad y resolución de problemas. En base a los resultados se presenta un modelo de integración de la competencia digital del docente universitario para su desarrollo profesional en la enseñanza de la matemática.The main purpose of this research is to explore the level of adopting e-competence of Math faculty staff at a university in Ecuador. It considers issues related to the availability of infrastructures, levels of technological training and use, integration and innovation of Web 2.0 tools in the teaching - learning processes of Mathematics, as a scenario for the growth and strengthening of the teacher professional development. Faculty staff needs not only technical and methodological strategies, but also has to be able to integrate technological tools in his teaching practices. The research methodology used is descriptive with a quantitative approach. The M-CDUECDD questionnaire was designed for gathering the information and it is structured in four dimensions and 196 variables which describe a preliminary profile on skills and indicators for Math professors. This instrument was validated by international experts in e-competences. The results show that university faculty in the area of mathematics has a medium average level of domain, use and innovation in five areas: Information and information literacy, communication and collaboration, digital content creation, security and problem solving. Based on the results, it is presented a model of integration of the digital competence of the university teacher for his professional development on Math teaching