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.
In the second category we find works whose main goal is to discover properties of online social networks. Sometimes companies provide the complete OSN dataset, e.g. @cite_1 studied the graph of a South Korean OSN, named CyWorld, and its scalability properties, in order to correctly estimate degree distribution and clustering coefficient in smaller samples. Leskovec @cite_27 , in his PhD thesis, studied dynamics of large social networks analyzing data provided by Microsoft about the usage of their instant messaging platform, formerly called MSN. More frequently, social network services companies like Facebook are reluctant to share their data for research purposes. The only viable solution is to acquire this information crawling the front-end of the social networks, wrapping and managing public accessible data. @cite_26 provided an exhaustive survey of these approaches, commonly referred to as Web Information Extraction techniques. @cite_13 defined a breadth-first-search sampling methodology applied to the Facebook social graph and formalized some properties as and , easily verifiable in small regions, but not generalizable to the whole graph.
@cite_13 @cite_27 @cite_26 @cite_1
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Our goal in this paper is to develop a practical framework for obtaining a uniform sample of users in an online social network (OSN) by crawling its social graph. Such a sample allows to estimate any user property and some topological properties as well. To this end, first, we consider and compare several candidate crawling techniques. Two approaches that can produce approximately uniform samples are the Metropolis-Hasting random walk (MHRW) and a re-weighted random walk (RWRW). Both have pros and cons, which we demonstrate through a comparison to each other as well as to the "ground truth." In contrast, using Breadth-First-Search (BFS) or an unadjusted Random Walk (RW) leads to substantially biased results. Second, and in addition to offline performance assessment, we introduce online formal convergence diagnostics to assess sample quality during the data collection process. We show how these diagnostics can be used to effectively determine when a random walk sample is of adequate size and quality. Third, as a case study, we apply the above methods to Facebook and we collect the first, to the best of our knowledge, representative sample of Facebook users. We make it publicly available and employ it to characterize several key properties of Facebook. 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. 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. 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. In this paper, we describe a comprehensive analysis of graph-theoretical properties of online social networks based on the Friend-of-a-Friend (FOAF) ontology. Of particular interest for this work were properties related to the small-world phenomenon. More than 1.6 million of the FOAF documents collected on the Semantic Web met our requirements and were analyzed in depth. Most FOAF documents are created and published by social networking services, blog hosting services, or combinations of the two as a matter of routine; only a fractional amount are maintained by individuals. Although the FOAF ontology defines unique identifiers for persons in theory, retrieval and particularly fusion of personal information is difficult and error-prone in practice. Nevertheless, we identified the largest strongly connected components of various community networks based on FOAF documents and analyzed them in regard to the small-world phenomenon. Interestingly, all components examined exhibited a characteristic path length comparable to the smallest length achievable for a graph of the respective size, and the clustering coefficient was much greater than expected for an equivalent random graph; along with power law degree distributions, both are typical features of small-world graphs. We study the structure of the social graph of active Facebook users, the largest social network ever analyzed. We compute numerous features of the graph including the number of users and friendships, the degree distribution, path lengths, clustering, and mixing patterns. Our results center around three main observations. First, we characterize the global structure of the graph, determining that the social network is nearly fully connected, with 99.91 of individuals belonging to a single large connected component, and we confirm the "six degrees of separation" phenomenon on a global scale. Second, by studying the average local clustering coefficient and degeneracy of graph neighborhoods, we show that while the Facebook graph as a whole is clearly sparse, the graph neighborhoods of users contain surprisingly dense structure. Third, we characterize the assortativity patterns present in the graph by studying the basic demographic and network properties of users. We observe clear degree assortativity and characterize the extent to which "your friends have more friends than you". Furthermore, we observe a strong effect of age on friendship preferences as well as a globally modular community structure driven by nationality, but we do not find any strong gender homophily. We compare our results with those from smaller social networks and find mostly, but not entirely, agreement on common structural network characteristics. 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. 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. 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 . 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. With their blistering expansions in recent years, popular on-line social sites such as Twitter, Facebook and Bebo, have become some of the major news sources as well as the most effective channels for viral marketing nowadays. However, alongside these promising features comes the threat of misinformation propagation which can lead to undesirable effects, such as the widespread panic in the general public due to faulty swine flu tweets on Twitter in 2009. Due to the huge magnitude of online social network (OSN) users and the highly clustered structures commonly observed in these kinds of networks, it poses a substantial challenge to efficiently contain viral spread of misinformation in large-scale social networks. In this paper, we focus on how to limit viral propagation of misinformation in OSNs. Particularly, we study a set of problems, namely the β1T -- Node Protectors, which aims to find the smallest set of highly influential nodes whose decontamination with good information helps to contain the viral spread of misinformation, initiated from the set I, to a desired ratio (1 − β) in T time steps. In this family set, we analyze and present solutions including inapproximability result, greedy algorithms that provide better lower bounds on the number of selected nodes, and a community-based heuristic method for the Node Protector problems. To verify our suggested solutions, we conduct experiments on real world traces including NetHEPT, NetHEPT_WC and Facebook networks. Empirical results indicate that our methods are among the best ones for hinting out those important nodes in comparison with other available methods. Modern applications such as web knowledge base, network traffic monitoring and online social networks have made available an unprecedented amount of network data with rich types of interactions carrying multiple attributes, for instance, port number and time tick in the case of network traffic. The design of algorithms to leverage this structured relationship with the power of computing to assist researchers and practitioners for better understanding, exploration and navigation of this space of information has become a challenging, albeit rewarding, topic in social network analysis and data mining. The constantly growing scale and enriching genres of network data always demand higher levels of efficiency, robustness and generalizability where existing approaches with successes on small, homogeneous network data are likely to fall short. We introduce MultiAspectForensics, a handy tool to automatically detect and visualize novel sub graph patterns within a local community of nodes in a heterogenous network, such as a set of vertices that form a dense bipartite graph whose edges share exactly the same set of attributes. We apply the proposed method on three data sets from distinct application domains, present empirical results and discuss insights derived from these patterns discovered. Our algorithm, built on scalable tensor analysis procedures, captures spectral properties of network data and reveals informative signals for subsequent domain-specific study and investigation, such as suspicious port-scanning activities in the scenario of cyber-security monitoring. We study the detailed growth of a social networking site with full temporal information by examining the creation process of each friendship relation that can collectively lead to the macroscopic properties of the network. We first study the reciprocal behavior of users, and find that link requests are quickly responded to and that the distribution of reciprocation intervals decays in an exponential form. The degrees of inviters accepters are slightly negatively correlative with reciprocation time. In addition, the temporal feature of the online community shows that the distributions of intervals of user behaviors, such as sending or accepting link requests, follow a power law with a universal exponent, and peaks emerge for intervals of an integral day. We finally study the preferential selection and linking phenomena of the social networking site and find that, for the former, a linear preference holds for preferential sending and reception, and for the latter, a linear preference also holds for preferential acceptance, creation, and attachment. Based on the linearly preferential linking, we put forward an analyzable network model which can reproduce the degree distribution of the network. The research framework presented in the paper could provide a potential insight into how the micro-motives of users lead to the global structure of online social networks. Social networking services are a fast-growing business in the Internet. However, it is unknown if online relationships and their growth patterns are the same as in real-life social networks. In this paper, we compare the structures of three online social networking services: Cyworld, MySpace, and orkut, each with more than 10 million users, respectively. We have access to complete data of Cyworld's ilchon (friend) relationships and analyze its degree distribution, clustering property, degree correlation, and evolution over time. We also use Cyworld data to evaluate the validity of snowball sampling method, which we use to crawl and obtain partial network topologies of MySpace and orkut. Cyworld, the oldest of the three, demonstrates a changing scaling behavior over time in degree distribution. The latest Cyworld data's degree distribution exhibits a multi-scaling behavior, while those of MySpace and orkut have simple scaling behaviors with different exponents. Very interestingly, each of the two e ponents corresponds to the different segments in Cyworld's degree distribution. Certain online social networking services encourage online activities that cannot be easily copied in real life; we show that they deviate from close-knit online social networks which show a similar degree correlation pattern to real-life social networks. Online Social Networks (OSNs) are popular platforms for interaction, communication and collaboration between friends. In this paper we develop and present a new platform to make interactions in OSNs accessible. Most of today's social networks, including Facebook, Twitter, and Google+ provide support for third party applications to use their social network graph and content. Such applications are strongly dependent on the set of software tools and libraries provided by the OSNs for their own development and growth. For example, third party companies like CNN provide recommendation materials based on user interactions and user's relationship graph. One of the limitations with this graph (or APIs) is the segregation from the shared content. We believe, and present in this paper, that the content shared and the actions taken on the content, creates a Social Interaction Network (SIN). As such, we extend Facebook's current API in order to allow applications to retrieve a weighted graph instead of Facebooks unweighted graph. Finally, we evaluate the proposed platform based on completeness and speed of the crawled results from selected community pages. We also give a few example uses of our API on how it can be used by third party applications. Building on the popularity of online social networks (OSNs) such as Facebook, social content-sharing applications allow users to form communities around shared interests. Millions of users worldwide use them to share recommendations on everything from music and books to resources on the web. However, their increasing popularity is beginning to attract the attention of malicious attackers. As social network credentials become valued targets of phishing attacks and social worms, attackers look to leverage compromised accounts for further financial gain. In this paper, we analyze the state of privacy protection in social content-sharing applications, describe effective privacy attacks against today's social networks, and propose anonymization techniques to protect users. We show that simple protection mechanisms such as anonymizing shared data can still leave users open to social intersection attacks, where a small number of compromised users can identify the originators of shared content. Modeling this as a graph anonymization problem, we propose to provide users with k-anonymity privacy guarantees by augmenting the social graph with "latent edges." We identify StarClique, a locally minimal graph structure required for users to attain k-anonymity, where at worst, a user is identified as one of k possible contributors of a data object. We prove the correctness of our approach using analysis. Finally, using experiments driven by traces from the del.icio.us social bookmark site, we demonstrate the practicality and effectiveness of our approach on real-world systems. Sampling from large graphs is an area of great interest, especially since the emergence of huge structures such as Online Social Networks (OSNs) and the World Wide Web (WWW). These networks, when viewed as graphs, often contain hundreds of millions of vertices and billions of edges. The large scale properties of a network can be summarized in terms of parameters of the underlying graph, such as the total number of vertices, edges and triangles. The large size of these networks makes it computationally expensive to obtain such structural properties of the underlying graph by exhaustive search. If we can estimate these properties by taking small but representative samples from the network, then size is no longer such a problem. In this paper we present a general framework to estimate network properties using random walks. These methods work under the assumption we are able to obtain local characteristics of a vertex during each step of the random walk, for example the number and labels of the neighboring vertices of a specific vertex These assumptions are relatively reasonable in practice, but may add some additional query cost to each step of the random walk. We also present some practical methods to estimate the total number of edges links m, number of vertices nodes n and number of connected triads of vertices (triangles) t in graphs with degree distributions which follow a power-law and higher number of triangles higher than expected in random graphs. We use these graphs since they tend to better correspond to the structure of large online networks, and in fact some of the data used are taken from such a network. Additionally we present experimental estimates for n, m, t we obtained using our methods on real or manufactured networks. In order to make the methods practical, the total number of steps made by the walk was limited to at most the size n of the network. In fact the results appear to converge for a lower number of steps, indicating that our proposed methods are feasible in practice. We investigate social networks of characters found in cultural works such as novels and films. These character networks exhibit many of the properties of complex networks such as skewed degree distribution and community structure, but may be of relatively small order with a high multiplicity of edges. Building on recent work of Beveridge and Shan [4], we consider graph extraction, visualization, and network statistics for three novels: Twilight by Stephanie Meyer, Steven King’s The Stand, and J.K. Rowling’s Harry Potter and the Goblet of Fire. Coupling with 800 character networks from films found in the http: moviegalaxies.com database, we compare the data sets to simulations from various stochastic complex networks models including random graphs with given expected degrees (also known as the Chung-Lu model), the configuration model, and the preferential attachment model. Using machine learning techniques based on motif (or small subgraph) counts, we determine that the Chung-Lu model best fits character networks and we conjecture why this may be the case. Online social networking sites have become increasingly popular over the last few years. As a result, new interdisciplinary research directions have emerged in which social network analysis methods are applied to networks containing hundreds millions of users. Unfortunately, links between individuals may be missing due to imperfect acquirement processes or because they are not yet reflected in the online network (i.e., friends in real world did not form a virtual connection.) Existing link prediction techniques lack the scalability required for full application on a continuously growing social network which may be adding everyday users with thousands of connections. The primary bottleneck in link prediction techniques is extracting structural features required for classifying links. In this paper we propose a set of simple, easy-to-compute structural features that can be analyzed to identify missing links. We show that a machine learning classifier trained using the proposed simple structural features can successfully identify missing links even when applied to a hard problem of classifying links between individuals who have at least one common friend. A new friends measure that we developed is shown to be a good predictor for missing links and an evaluation experiment was performed on five large social networks datasets: Face book, Flickr, You Tube, Academia and The Marker. Our methods can provide social network site operators with the capability of helping users to find known, offline contacts and to discover new friends online. They may also be used for exposing hidden links in an online social network. This paper reviews the state of the art and some emerging issues in research areas related to pattern analysis and monitoring of web-based social communities. This research area is important for several reasons. First, the presence of near-ubiquitous low-cost computing and communication technologies has enabled people to access and share information at an unprecedented scale. The scale of the data necessitates new research for making sense of such content. Furthermore, popular websites with sophisticated media sharing and notification features allow users to stay in touch with friends and loved ones; these sites also help to form explicit and implicit social groups. These social groups are an important source of information to organize and to manage multimedia data. In this article, we study how media-rich social networks provide additional insight into familiar multimedia research problems, including tagging and video ranking. In particular, we advance the idea that the contextual and social aspects of media are as important for successful multimedia applications as is the media content. We examine the inter-relationship between content and social context through the prism of three key questions. First, how do we extract the context in which social interactions occur? Second, does social interaction provide value to the media object? Finally, how do social media facilitate the repurposing of shared content and engender cultural memes? We present three case studies to examine these questions in detail. In the first case study, we show how to discover structure latent in the social media data, and use the discovered structure to organize Flickr photo streams. In the second case study, we discuss how to determine the interestingness of conversations—and of participants—around videos uploaded to YouTube. Finally, we show how the analysis of visual content, in particular tracing of content remixes, can help us understand the relationship among YouTube participants. For each case, we present an overview of recent work and review the state of the art. We also discuss two emerging issues related to the analysis of social networks—robust data sampling and scalable data analysis.