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
2047443612 2275735758 2148317291 2121761994
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. 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. 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. Online Social Networks (OSN) during last years acquired a huge and increasing popularity as one of the most important emerging Web phenomena, deeply modifying the behavior of users and contributing to build a solid substrate of connections and relationships among people using the Web. In this preliminary work paper, our purpose is to analyze Facebook, considering a signi�cant sample of data re ecting relationships among subscribed users. Our goal is to extract, from this platform, relevant information about the distribution of these relations and exploit tools and algorithms provided by the Social Network Analysis (SNA) to discover and, possibly, understand underlying similarities between the developing of OSN and real-life social networks. Social networks are popular platforms for interaction, communication and collaboration between friends. Researchers have recently proposed an emerging class of applications that leverage relationships from social networks to improve security and performance in applications such as email, web browsing and overlay routing. While these applications often cite social network connectivity statistics to support their designs, researchers in psychology and sociology have repeatedly cast doubt on the practice of inferring meaningful relationships from social network connections alone. This leads to the question: Are social links valid indicators of real user interaction? If not, then how can we quantify these factors to form a more accurate model for evaluating socially-enhanced applications? In this paper, we address this question through a detailed study of user interactions in the Facebook social network. We propose the use of interaction graphs to impart meaning to online social links by quantifying user interactions. We analyze interaction graphs derived from Facebook user traces and show that they exhibit significantly lower levels of the "small-world" properties shown in their social graph counterparts. This means that these graphs have fewer "supernodes" with extremely high degree, and overall network diameter increases significantly as a result. To quantify the impact of our observations, we use both types of graphs to validate two well-known social-based applications (RE and SybilGuard). The results reveal new insights into both systems, and confirm our hypothesis that studies of social applications should use real indicators of user interactions in lieu of social graphs. We present a study of anonymized data capturing a month of high-level communication activities within the whole of the Microsoft Messenger instant-messaging system. We examine characteristics and patterns that emerge from the collective dynamics of large numbers of people, rather than the actions and characteristics of individuals. The dataset contains summary properties of 30 billion conversations among 240 million people. From the data, we construct a communication graph with 180 million nodes and 1.3 billion undirected edges, creating the largest social network constructed and analyzed to date. We report on multiple aspects of the dataset and synthesized graph. We find that the graph is well-connected and robust to node removal. We investigate on a planetary-scale the oft-cited report that people are separated by "six degrees of separation" and find that the average path length among Messenger users is 6.6. We find that people tend to communicate more with each other when they have similar age, language, and location, and that cross-gender conversations are both more frequent and of longer duration than conversations with the same gender. 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. 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. Given multimillion-node graphs such as "who-follows-whom", "patent-cites-patent", "user-likes-page" and "actor director-makes-movie" networks, how can we find unexpected behaviors? When companies operate on the graphs with monetary incentives to sell Twitter "Followers" and Facebook page "Likes", the graphs show strange connectivity patterns. In this paper, we study a complete graph from a large Twitter-style social network, spanning up to 3.33 billion edges. We report strange deviations from typical patterns like smooth degree distributions. We find that such deviations are often due to "lockstep behavior" that large groups of followers connect to the same groups of followees. Our first contribution is that we study strange patterns on the adjacency matrix and in the spectral subspaces with respect to several flavors of lockstep. We discover that (a) the lockstep behaviors on the graph shape dense "block" in its adjacency matrix and creates "rays" in spectral subspaces, and (b) partially overlapping of the behaviors shape "staircase" in its adjacency matrix and creates "pearls" in spectral subspaces. The second contribution is that we provide a fast algorithm, using the discovery as a guide for practitioners, to detect users who offer the lockstep behaviors in undirected directed bipartite graphs. We carry out extensive experiments on both synthetic and real datasets, as well as public datasets from IMDb and US Patent. The results demonstrate the scalability and effectiveness of our proposed algorithm. Breadth First Search (BFS) and other graph traversal techniques are widely used for measuring large unknown graphs, such as online social networks. It has been empirically observed that incomplete BFS is biased toward high degree nodes. In contrast to more studied sampling techniques, such as random walks, the bias of BFS has not been characterized to date. In this paper, we quantify the degree bias of BFS sampling. In particular, we calculate the node degree distribution expected to be observed by BFS as a function of the fraction of covered nodes, in a random graph RG(p k ) with a given (and arbitrary) degree distribution p k . Furthermore, we also show that, for RG(p k ), all commonly used graph traversal techniques (BFS, DFS, Forest Fire, and Snowball Sampling) lead to the same bias, and we show how to correct for this bias. To give a broader perspective, we compare this class of exploration techniques to random walks that are well-studied and easier to analyze. Next, we study by simulation the effect of graph properties not captured directly by our model. We find that the bias gets amplified in graphs with strong positive assortativity. Finally, we demonstrate the above results by sampling the Facebook social network, and we provide some practical guidelines for graph sampling in practice. 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. 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. With more than 250 million active users, Facebook (FB) is currently one of the most important online social networks. Our goal in this paper is to obtain a representative (unbiased) sample of Facebook users by crawling its social graph. In this quest, we consider and implement several candidate techniques. Two approaches that are found to perform well 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" (UNI - obtained through true uniform sampling of FB userIDs). In contrast, the traditional Breadth-First-Search (BFS) and Random Walk (RW) perform quite poorly, producing substantially biased results. 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 can be used to effectively determine when a random walk sample is of adequate size and quality for subsequent use (i.e., when it is safe to cease sampling). Using these methods, we collect the first, to the best of our knowledge, unbiased sample of Facebook. Finally, we use one of our representative datasets, collected through MHRW, to characterize several key properties of Facebook. The high level of dynamics in today's online social networks (OSNs) creates new challenges for their infrastructures and providers. In particular, dynamics involving edge creation has direct implications on strategies for resource allocation, data partitioning and replication. Understanding network dynamics in the context of physical time is a critical first step towards a predictive approach towards infrastructure management in OSNs. Despite increasing efforts to study social network dynamics, current analyses mainly focus on change over time of static metrics computed on snapshots of social graphs. The limited prior work models network dynamics with respect to a logical clock. In this paper, we present results of analyzing a large timestamped dataset describing the initial growth and evolution of a large social network in China. We analyze and model the burstiness of link creation process, using the second derivative, i.e. the acceleration of the degree. This allows us to detect bursts, and to characterize the social activity of a OSN user as one of four phases: acceleration at the beginning of an activity burst, where link creation rate is increasing; deceleration when burst is ending and link creation process is slowing; cruising, when node activity is in a steady state, and complete inactivity. 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. 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. Social network analysis and mining has been highly influenced by the online social web sites, telecom consumer data and instant messaging systems and has widely analyzed the presence of dense communities using graph theory and machine learning techniques. Mobile social network analysis is the mapping and measuring of interactions and flows between people, groups, and organizations based on the usage of their mobile communication services. Community identification and mining is one of the recent major directions in social network analysis. In this paper we find the communities in the network based on a modularity factor. Then we propose a graph theory-based algorithm for further split of communities resulting in smaller sized and closely knit sub-units, to drill down and understand consumer behavior in a comprehensive manner. These sub-units are then analyzed and labeled based on their group behavior pattern. The analysis is done using two approaches:—rule-based, and cluster-based, for comparison and the able usage of information for suitable labeling of groups. Moreover, we measured and analyzed the uniqueness of the structural properties for each small unit; it is another quick and dynamic way to assign suitable labels for each distinct group. We have mapped the behavior-based labeling with unique structural properties of each group. It reduces considerably the time taken for processing and identifying smaller sub-communities for effective targeted marketing. The efficiency of the employed algorithms was evaluated on a large telecom dataset in three different stages of our work. Advertising is ubiquitous on the Web; numerous ad networks serve billions of ads daily via keyword or search term auctions. Recently, online social networks (OSNs) such as Facebook have created site-specific ad services that differ from traditional ad networks by letting advertisers bid on users rather than keywords. With Facebook's annual ad revenue exceeding $4 billion, OSN-based ad services are emerging to be a significant fraction of the online ad market. In contrast to other online ad markets (e.g., Google's ad market), there has been little academic study of OSN ad services, and OSNs have released very little data about their advertising markets; as a result, researchers currently lack the tools to measure and understand these markets. In this paper, our goal is to bring visibility to OSN ad markets, focusing on Facebook. We demonstrate that the (undocumented) feature that suggests bids to advertisers is most likely calculated via sampling recent winning bids. We then show how this feature can be used to explore the relative value of different user demographics and the overall stability of the advertising market. Through the exploration of suggested bid data for different demographics, we find dramatic differences in prices paid across different user interests and locations. Finally, we show that the ad market shows long-term variability, suggesting that OSN ad services have yet to mature. 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. This thesis focuses on the identification of influential users, also known as key opinion leaders, within the social network Instagram. Instagram is a very popular platform to share images with the option to categorise the images by certain tags. It is possible to collect public data from Instagram via the open API of the platform. This thesis presents a concept to create an automated crawler for this API and col- lect data into a database in order to apply algorithms from graph theory to identify opinion leaders afterwards. The sample topic for this thesis has been veganfood and all associated posts from Instagram have been crawled. After the user data has been crawled a graph has been created to do further research with common social network analysis tools. The graph contained a total set of more than 26,000 nodes. To identify opinion leaders from this graph, five di↵erent metrics have been applied, in particular PageRank, Betweenness centrality, Closeness Centrality, Degree and Eigen- vector centrality. After applying the di↵erent algorithms the results have been eval- uated and additionally an marketing expert with focus on social media analysed the results. This project was able to figured out that it is possible to find opinion leaders by using the PageRank algorithm and that those opinion leaders have a very good value of en- gagement. This indicates that they show a high interaction with other users on their posts. In conclusion the additional research options are discussed to provide a future outlook.