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.
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.
@cite_30 @cite_0 @cite_19 @cite_23 @cite_5 @cite_34
2056278891 2110903679 2166639727 1984156542 2112397748 1973846084 2094858374 2406604473 2287683866 2033268855 2560545764 2152046791 2138600229 2191033676 2024814977 2056732180 2420733993 1965759835 2148847267 1967323898
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. Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior, and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions, passive actions such as profile browsing that cannot be observed by traditional measurement techniques. In this paper, we seek a deeper understanding of both visible and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 150 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed visitor logs for each user profile, and counters for each photo and diary blog entry. We capture detailed histories of profile visits over a period of 90 days for more than 61,000 users in the Peking University Renren network, and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than visible events, non-reciprocal in nature, and that profile popularity are uncorrelated with the frequency of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior, and compare their structural properties against those of both visible interaction graphs and social graphs. Online social networks (OSNs) recommend new friends to registered users based on local-based features of the graph (i.e. based on the number of common friends that two users share). However, OSNs do not exploit all different length paths of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global-based approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-sized social networks. In this paper we provide friend recommendations, also known as the link prediction problem, by traversing all paths of a limited length, based on the ''algorithmic small world hypothesis''. As a result, we are able to provide more accurate and faster friend recommendations. We also derive variants of our method that apply to different types of networks (directed undirected and signed unsigned). We perform an extensive experimental comparison of the proposed method against existing link prediction algorithms, using synthetic and three real data sets (Epinions, Facebook and Hi5). We also show that a significant accuracy improvement can be gained by using information about both positive and negative edges. Finally, we discuss extensively various experimental considerations, such as a possible MapReduce implementation of FriendLink algorithm to achieve scalability. We present analytical models and simulation results that characterize the impacts of the following factors on the propagation of cross-site scripting (XSS) worms in online social networks (OSNs): 1) user behaviors, namely, the probability of visiting a friend's profile versus a stranger's; 2) the highly clustered structure of communities; and 3) community sizes. Our analyses and simulation results show that the clustered structure of a community and users' tendency to visit their friends more often than strangers help slow down the propagation of XSS worms in OSNs. We then present a study of selective monitoring schemes that are more resource efficient than the exhaustive checking approach used by the Facebook detection system which monitors every possible read and write operation of every user in the network. The studied selective monitoring schemes take advantage of the characteristics of OSNs such as the highly clustered structure and short average distance to select only a subset of strategically placed users to monitor, thus minimizing resource usage while maximizing the monitoring coverage. We present simulation results to show the effectiveness of the studied selective monitoring schemes for XSS worm detection. Online social networks (OSNs) like Facebook, and Myspace recommend new friends to registered users based on local features of the graph (i.e. based on the number of common friends that two users share). However, OSNs do not exploit the whole structure of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-size social networks. In this paper, we define a basic node similarity measure that captures effectively local graph features. We also exploit global graph features introducing transitive node similarity. Moreover, we derive variants of our method that apply in signed networks. We perform extensive experimental comparison of the proposed method against existing recommendation algorithms using synthetic and real data sets (Facebook, Hi5 and Epinions). Our experimental results show that our FriendTNS algorithm outperforms other approaches in terms of accuracy and it is also time efficient. We show that a significant accuracy improvement can be gained by using information about both positive and negative edges. Extensive research has been conducted on top of online social networks (OSNs), while little attention has been paid to the data collection process. Due to the large scale of OSNs and their privacy control policies, a partial data set is often used for analysis. The data set analyzed is decided by many factors including the choice of seeds, node selection algorithms, and the sample size. These factors may introduce biases and further contaminate or even skew the results. To evaluate the impact of different factors, this paper examines the OSN graph crawling problem, where the nodes are OSN users and the edges are the links (or relationship) among these users. More specifically, by looking at various factors in the crawling process, the following problems are addressed in this paper:* Efficiency: How fast different crawlers discover nodes links;* Sensitivity: How different OSNs and the number of protected users affect crawlers;* Bias: How major graph properties are skewed.To the best of our knowledge, our simulations on four real world online social graphs provide the first in-depth empirical answers to these questions. This paper intends to provide some insights of a scientific problem: how likely one's interests can be inferred from his her social connections -- friends, friends' friends, 3-degree friends, etc? Is "Birds of a Feather Flocks Together" a norm? We do not consider the friending activity on online social networking sites. Instead, we conduct this study by implementing a privacy-preserving large distribute social sensor system in a large global IT company to capture the multifaceted activities of 30,000+ people, including communications (e.g., emails, instant messaging, etc) and Web 2.0 activities (e.g., social bookmarking, file sharing, blogging, etc). These activities occupy the majority of employees' time in work, and thus, provide a high quality approximation to the real social connections of employees in the workplace context. In addition to such "informal networks", we investigated the "formal networks", such as their hierarchical structure, as well as the demographic profile data such as geography, job role, self-specified interests, etc. Because user ID matching across multiple sources on the Internet is very difficult, and most user activity logs have to be anonymized before they are processed, no prior studies could collect comparable multifaceted activity data of individuals. That makes this study unique. In this paper, we present a technique to predict the inference quality by utilizing (1) network analysis and network autocorrelation modeling of informal and formal networks, and (2) regression models to predict user interest inference quality from network characteristics. We verify our findings with experiments on both implicit user interests indicated by the content of communications or Web 2.0 activities, and explicit user interests specified in user profiles. We demonstrate that the inference quality prediction increases the inference quality of implicit interests by 42.8 , and inference quality of explicit interests by up to 101 . Online Social Networks (OSNs) encourage users to create an online presence that reflects their offline identity. OSNs create the illusion that these online accounts correspond to the correct offline person, but in reality the OSN lacks the resources to detect impersonation. We propose that OSN users identify each other based on interaction and experience. We believe that impersonation can be thwarted by users who possess exclusive shared knowledge, secret information shared only between a pair of OSN friends. We describe existing protocols that use shared secrets to exchange public keys without revealing those secrets to attackers. We present results from a user study on Facebook to show that users do share exclusive knowledge with their Facebook friends and attackers are rarely able to guess that knowledge. Finally, we show that friend identification can be extended using a web of trust built on the OSN friend graph. A fundamental question when studying underlying friendship and interaction graphs of Online Social Networks (OSNs) is how closely these graphs mirror real-world associations. The presence of phantom or fake profiles dilutes the integrity of this correspondence. This paper looks at the presence of phantom profiles in the context of social gaming, i.e., profiles created with the purpose of gaining a strategic advantage in social games. Through a measurement-based study of a Facebook gaming application that is known to attract phantom profiles, we show statistical differences among a subset of features associated with genuine and phantom profiles. We then use supervised learning to classify phantom profiles. Our work represents a first-step towards solving the more general problem of detecting fake phantom identities in OSNs. Online social networks (OSNs) have become popular destinations for connecting friends and sharing information. Recent statistics suggest that OSN users regularly share contents from video sites, and a significant amount of requests of the video sites are indeed from them nowadays. These behaviors have substantially changed the workload of online video services. To better understand this paradigm shift, we conduct a long-term and extensive measurement of video sharing in RenRen, the largest Facebook-like OSN in China. In this paper, we focus on the video popularity distribution and evolution. In particular, we find that the video popularity distribution exhibits perfect power-law feature (while videos in YouTube exhibit a power-law waist with a long truncated tail). Moreover, we observe that the requests for the new published videos generally experience two or three days latency to reach the peak value, and then change dynamically with a series of unpredictable bursts (while in YouTube, videos reach the global peak immediately after introduction to the system, and then the accesses generally decrease overtime, except possibly on some special days). These differences can raise new challenges to content providers. For example, the video popularity is now hard to predict based on their historical requests. We further develop a simple yet effective model to simulate user requests process across videos in OSNs. Trace-based simulation shows that it can well capture the observed features. Due to the amount of personally identiable information shared by users of online social networks (OSNs) and the often not adequately adjusted privacy settings, it is possible to identify a user’s several OSN proles. In this paper, we illustrate that third parties just have to take a small step to link OSN proles of a user and, consequently, to aggregate various pieces of information shared in several OSNs. Particularly, based on statistical results we illustrate how the often publicly available friends lists can be exploited to link several OSN proles of a single natural person. The results presented in this paper show that proles can be linked even at low cost, i.e., without complex correlation techniques or high computational power. To assess the risk of privacy leakage by prole linking, we, additionally, report how often specic pieces of information are made publicly available by users in four of the most popular OSNs. We show that users tend to publish dierent pieces of information in dierent OSNs and, thereby, demonstrate that by linking friends lists more information about a user can be gained than the user shared in a single OSN. For the study we analyzed more than 180,000 user proles and compared more than seven million pairs of proles to investigate prole linkability. State-of-the-art techniques for probability sampling of users of online social networks (OSNs) are based on random walks on a single social relation (typically friendship). While powerful, these methods rely on the social graph being fully connected. Furthermore, the mixing time of the sampling process strongly depends on the characteristics of this graph. In this paper, we observe that there often exist other relations between OSN users, such as membership in the same group or participation in the same event. We propose to exploit the graphs these relations induce, by performing a random walk on their union multigraph. We design a computationally efficient way to perform multigraph sampling by randomly selecting the graph on which to walk at each iteration. We demonstrate the benefits of our approach through (i) simulation in synthetic graphs, and (ii) measurements of Last.fm- an Internet website for music with social networking features. More specifically, we show that multigraph sampling can obtain a representative sample and faster convergence, even when the individual graphs fail, i.e., are disconnected or highly clustered. Various online social networks (OSNs) have been developed rapidly on the Internet. Researchers have analyzed different properties of such OSNs, mainly focusing on the formation and evolution of the networks as well as the information propagation over the networks. In knowledge-sharing OSNs, such as blogs and question answering systems, issues on how users participate in the network and how users "generate contribute" knowledge are vital to the sustained and healthy growth of the networks. However, related discussions have not been reported in the research literature. In this work, we empirically study workloads from three popular knowledge-sharing OSNs, including a blog system, a social bookmark sharing network, and a question answering social network to examine these properties. Our analysis consistently shows that (1) users' posting behavior in these networks exhibits strong daily and weekly patterns, but the user active time in these OSNs does not follow exponential distributions; (2) the user posting behavior in these OSNs follows stretched exponential distributions instead of power-law distributions, indicating the influence of a small number of core users cannot dominate the network; (3) the distributions of user contributions on high-quality and effort-consuming contents in these OSNs have smaller stretch factors for the stretched exponential distribution. Our study provides insights into user activity patterns and lays out an analytical foundation for further understanding various properties of these OSNs. In the last decade, Online Social Networks (OSNs) have taken the world by storm. They range from superficial to professional, from focused to general-purpose, and, from free-form to highly structured. Numerous people have multiple accounts within the same OSN and even more people have an account on more than one OSN. Since all OSNs involve some amount of user input, often in written form, it is natural to consider whether multiple incarnations of the same person in various OSNs can be effectively correlated or linked. One intuitive means of linking accounts is by using stylometric analysis. This paper reports on (what we believe to be) the first trilateral large-scale stylometric OSN linkability study. Its outcome has important implications for OSN privacy. The study is trilateral since it involves three OSNs with very different missions: (1) Yelp, known primarily for its user-contributed reviews of various venues, e.g, dining and entertainment, (2) Twitter, popular for its pithy general-purpose micro-blogging style, and (3) Flickr, used exclusively for posting and labeling (describing) photographs. As our somewhat surprising results indicate, stylometric linkability of accounts across these heterogeneous OSNs is both viable and quite effective. The main take-away of this work is that, despite OSN heterogeneity, it is very challenging for one person to maintain privacy across multiple active accounts on different OSNs. Online Social Networks (OSNs) have recently emerged as one of the most effective channels for information sharing and discovery due to their ability of allowing users to read and create new content simultaneously. While this advantage provides users more rooms to decide which content to follow, it also makes OSNs fertile grounds for the wide spread of misinformation which can lead to undesirable consequences. In order to guarantee the trustworthiness of content sharing in OSNs, it is thus essential to have a strategic investigation on the first and foremost concern: the sources of misinformation. In this paper, we study k-Suspector problem which aims to identify the top k most suspected sources of misinformation. We propose two effective approaches namely ranking-based and optimization-based algorithms. We further extend our solutions to cope with the incompleteness of collected data as well as multiple attacks, which mostly occur in reality. Experimental results on real-world datasets show that our approaches achieve competitive detection ratios in a timely manner in comparison with available methods. In Service-Oriented Computing environments, there is a large number of service providers providing a variety of services to service customers. Conventional recommender systems, which adopt the information filtering techniques, can be used to automatically generate recommendations of service providers to service customers who are also the system users. However, data sparsity and trust enhancement are the traditional problems in recommender systems. Targeting the data sparsity problem, recent studies on recommender systems have started to leverage information from online social networks to collect recommendations from more participants and derive the final recommendation. However, this requires the methods to infer the trust between participants without any direct interactions in online social networks, which should take into account both the social context of participants and the context of the target services to be recommended, for trust enhanced recommendations. In this paper, we first present a contextual social network model that takes into account both participants' personal characteristics (referred to as the independent social context, including preference and expertise in domains) and mutual relations (referred to as the dependent social context, including the trust, social intimacy, and interaction context between two participants). In addition, we propose a new probabilistic approach, SocialTrust, as the first solution in the literature, to social context-aware trust inference in social networks. The result delivered by this approach is particularly important in evaluating the trust from a source participant to an end recommender who recommends a target service or service provider, via the sub-network consisting of intermediate participants recommenders between them and relevant contextual information. Moreover, we propose algorithms that consider cycles and information updates in social networks. Experiments demonstrate that our approach is effective and superior to existing trust inference methods, and can deliver more reasonable and trustworthy results. The proposed algorithms considering cycles and information updates in social networks are efficient and applicable to real social networks. 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 develop approaches to link prediction based on measures the "proximity" of nodes in a network. Experiments on large co-authorship 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. Online Social Networks (OSNs) have recently emerged as an effective medium for information sharing. Unfortunately, it has been frequently observed that malicious rumors being spread over an OSN are not controllable, and this is not desirable. This paper proposes a new problem, namely the γ - k rumor restriction problem, whose goal is, given a social network, to find a set S of nodes with k protectors (γ * k protectors from the contaminated set, and (1 - γ) * k protectors from the decontaminated set) to protect the network such that the number of decontaminated nodes is maximum. We show that the objective function of the γ - k rumor restriction problem is submodular, and use this result to design a greedy approximation algorithm with performance ratio of 1 - 1 e for the problem under the linear threshold model and independent cascade model, respectively. To verify our algorithms, we conduct experiments on real word social networks including NetHEPT, WikiVote and Slashdot0811. The results show that our algorithm works efficiently and effectively. 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. We consider the problem of identifying the most respected, authoritative members of a large-scale online social network (OSN) by constructing a global ranked list of its members. The problem is distinct from the problem of identifying influencers: we are interested in identifying members who are influential in the real world, even when not necessarily so on the OSN. We focus on two sources for information about user authority: (a) invitations to connect, which are usually sent to people whom the inviter respects, and (b) members' browsing behavior, as profiles of more important people are viewed more often than others'. We construct two directed graphs over the same set of nodes (representing member profiles): the invitation graph and the navigation graph respectively. We show that the standard PageRank algorithm, a baseline in web page ranking, is not effective in people ranking, and develop a social capital based model, called the fair bets model, as a viable solution. We then propose a novel approach, called bimodal fair bets, for combining information from two (or more) endorsement graphs drawn from the same OSN, by simultaneously using the authority scores of nodes in one graph to inform the other, and vice versa, in a mutually reinforcing fashion. We evaluate the ranking results on the LinkedIn social network using this model, where members who have Wikipedia profiles are assumed to be authoritative. Experimental results show that our approach outperforms the baseline approach by a large margin.