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_30: Facebook is one of the most popular Internet sites today. A key feature that arguably contributed to Facebook's unprecedented success is its application platform, which enables the development of third-party social-networking applications. Understanding how these applications are installed and used is important for the function and utility of web-based online social networks, e.g. to better engineer them and or to design advertising campaigns. In this paper, we characterize the popularity and user reach of Facebook applications. We analyze application usage data gathered over a period of six months from Facebook and Adonomics - a Facebook analytics service. We also crawl publicly accessible Facebook user profiles and obtain per-user application installation statistics, for approximately 300K users and 13.6K applications. Our findings include that (i) the popularity of Facebook applications has a highly skewed distribution; (ii) although the total number of application installations increases with time, the average user activity decreases; and (iii) users with more applications installed are more likely to install new applications.
  @cite_0: Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link-prediction problem, and we develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a network. Experiments on large coauthorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures. © 2007 Wiley Periodicals, Inc.
  @cite_19: Online social networks pose an interesting problem: how to best characterize the different classes of user behavior. Traditionally, user behavior characterization methods, based on user individual features, are not appropriate for online networking sites. In these environments, users interact with the site and with other users through a series of multiple interfaces that let them to upload and view content, choose friends, rank favorite content, subscribe to users and do many other interactions. Different interaction patterns can be observed for different groups of users. In this paper, we propose a methodology for characterizing and identifying user behaviors in online social networks. First, we crawled data from YouTube and used a clustering algorithm to group users that share similar behavioral pattern. Next, we have shown that attributes that stem from the user social interactions, in contrast to attributes relative to each individual user, are good discriminators and allow the identification of relevant user behaviors. Finally, we present and discuss experimental results of the use of proposed methodology. A set of useful profiles, derived from the analysis of the YouTube sample is presented. The identification of different classes of user behavior has the potential to improve, for instance, recommendation systems for advertisements in online social networks.
  @cite_23: The growth of Web-based social networking and the properties of those networks have created great potential for producing intelligent software that integrates a user's social network and preferences. Our research looks particularly at assigning trust in Web-based social networks and investigates how trust information can be mined and integrated into applications. This article introduces a definition of trust suitable for use in Web-based social networks with a discussion of the properties that will influence its use in computation. We then present two algorithms for inferring trust relationships between individuals that are not directly connected in the network. Both algorithms are shown theoretically and through simulation to produce calculated trust values that are highly accurate.. We then present TrustMail, a prototype email client that uses variations on these algorithms to score email messages in the user's inbox based on the user's participation and ratings in a trust network.
  @cite_5: Online Social Networks (OSNs) have already attracted more than half a billion users. However, our understanding of which OSN features attract and keep the attention of these users is poor. Studies thus far have relied on surveys or interviews of OSN users or focused on static properties, e. g., the friendship graph, gathered via sampled crawls. In this paper, we study how users actually interact with OSNs by extracting clickstreams from passively monitored network traffic. Our characterization of user interactions within the OSN for four different OSNs (Facebook, LinkedIn, Hi5, and StudiVZ) focuses on feature popularity, session characteristics, and the dynamics within OSN sessions. We find, for example, that users commonly spend more than half an hour interacting with the OSNs while the byte contributions per OSN session are relatively small.
  @cite_34: Understanding how users behave when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present a first of a kind analysis of user workloads in online social networks. Our study is based on detailed clickstream data, collected over a 12-day period, summarizing HTTP sessions of 37,024 users who accessed four popular social networks: Orkut, MySpace, Hi5, and LinkedIn. The data were collected from a social network aggregator website in Brazil, which enables users to connect to multiple social networks with a single authentication. Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites. Additionally, we crawled the social network topology of Orkut, so that we could analyze user interaction data in light of the social graph. Our data analysis suggests insights into how users interact with friends in Orkut, such as how frequently users visit their friends' or non-immediate friends' pages. In summary, our analysis demonstrates the power of using clickstream data in identifying patterns in social network workloads and social interactions. Our analysis shows that browsing, which cannot be inferred from crawling publicly available data, accounts for 92 of all user activities. Consequently, compared to using only crawled data, considering silent interactions like browsing friends' pages increases the measured level of interaction among users.
summary: Finally, literature on analysis of the behavior of OSNs users is abundant: Golbeck and Hendler @cite_23 described the evolution of trust relationships among them. Liben-nowell and Kleinberg @cite_0 studied the link-prediction problem, formalizing the concept of proximity of networks users. Several studies characterized OSNs users behaviors @cite_30 @cite_19 @cite_34 , thus @cite_5 provided a starting point illustrating how to analyze OSNs (in particular Facebook) from a network perspective.