text: We describe our work in the collection and analysis of massive data describing the connections between participants to online social networks. Alternative approaches to social network data collection are defined and evaluated in practice, against the popular Facebook Web site. Thanks to our ad-hoc, privacy-compliant crawlers, two large samples, comprising millions of connections, have been collected; the data is anonymous and organized as an undirected graph. We describe a set of tools that we developed to analyze specific properties of such social-network graphs, i.e., among others, degree distribution, centrality measures, scaling laws and distribution of friendship.
  @cite_15: Acknowledgements 1. Introduction Stanley Wasserman, John Scott and Peter J. Carrington 2. Recent developments in network measurement Peter V. Marsden 3. Network sampling and model fitting Ove Frank 4. Extending centrality Martin Everett and Stephen P. Borgatti 5. Positional analyses of sociometric data Patrick Doreian, Vladimir Batagelj and Anuska Ferligoj 6. Network models and methods for studying the diffusion of innovations Thomas W. Valente 7. Using correspondence analysis for joint displays of affiliation networks Katherine Faust 8. An introduction to random graphs, dependence graphs, and p* Stanley Wasserman and Garry Robins 9. Random graph models for social networks: multiple relations or multiple raters Laura M. Koehly and Philippa Pattison 10. Interdependencies and social processes: dependence graphs and generalized dependence structures Garry Robins and Philippa Pattison 11. Models for longitudinal network data Tom A. B. Snijders 12. Graphical techniques for exploring social network data Linton C. Freeman 13. Software for social network analysis Mark Huisman and Marijtje A. J. van Duijn Index.
  @cite_21: In this paper, we consider the evolution of structure within large online social networks. We present a series of measurements of two such networks, together comprising in excess of five million people and ten million friendship links, annotated with metadata capturing the time of every event in the life of the network. Our measurements expose a surprising segmentation of these networks into three regions: singletons who do not participate in the network; isolated communities which overwhelmingly display star structure; and a giant component anchored by a well-connected core region which persists even in the absence of stars.We present a simple model of network growth which captures these aspects of component structure. The model follows our experimental results, characterizing users as either passive members of the network; inviters who encourage offline friends and acquaintances to migrate online; and linkers who fully participate in the social evolution of the network.
  @cite_32: Online social networking sites like Orkut, YouTube, and Flickr are among the most popular sites on the Internet. Users of these sites form a social network, which provides a powerful means of sharing, organizing, and finding content and contacts. The popularity of these sites provides an opportunity to study the characteristics of online social network graphs at large scale. Understanding these graphs is important, both to improve current systems and to design new applications of online social networks. This paper presents a large-scale measurement study and analysis of the structure of multiple online social networks. We examine data gathered from four popular online social networks: Flickr, YouTube, LiveJournal, and Orkut. We crawled the publicly accessible user links on each site, obtaining a large portion of each social network's graph. Our data set contains over 11.3 million users and 328 million links. We believe that this is the first study to examine multiple online social networks at scale. Our results confirm the power-law, small-world, and scale-free properties of online social networks. We observe that the indegree of user nodes tends to match the outdegree; that the networks contain a densely connected core of high-degree nodes; and that this core links small groups of strongly clustered, low-degree nodes at the fringes of the network. Finally, we discuss the implications of these structural properties for the design of social network based systems.
  @cite_8: Online social networks have become major and driving phenomena on the Web. In this talk, we will address key modeling and algorithmic questions related to large online social networks. From the modeling perspective, we raise the question of whether there is a generative model for network evolution. The availability of time-stamped data makes it possible to study this question at an extremely fine granularity. We exhibit a simple, natural model that leads to synthetic networks with properties similar to the online ones. From an algorithmic viewpoint, we focus on data mining challenges posed by the magnitude of data in these networks. In particular, we examine topics related to influence and correlation in user activities and compressibility of such networks.
summary: Work belonging to the last category usually present SNA techniques applied to OSNs. @cite_15 formalized a rigorous methodology to model OSNs, in particular to discover, if existing, aggregations of nodes covering particular positions in the graph (e.g. centrality positions) and clusters of particular relevance. @cite_8 @cite_21 focused on analyzing the structure of the of the graph, trying to define a generative model to describe the evolution of the network; they also introduced techniques of comparison of simulations against actual data to verify the reliability of the model. @cite_32 illustrated several aspects of the analysis of OSNs; they crawled large OSNs, then analyzed several networks using methods formalized in the SNA, e.g. inspecting link symmetry, power-law node degrees, groups formation, etc.