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.

Online social networks pose significant challenges to computer scientists, physicists, and sociologists alike, for their massive size, fast evolution, and uncharted potential for social computing. One particular problem that has interested us is community identification. Many algorithms based on various metrics have been proposed for identifying communities in networks [18, 24], but a few algorithms scale to very large networks. Three recent community identification algorithms, namely CNM [16], Wakita [59], and Louvain [10], stand out for their scalability to a few millions of nodes. All of them use modularity as the metric of optimization. However, all three algorithms produce inconsistent communities every time the input ordering of nodes to the algorithms changes. We propose two quantitative metrics to represent the level of consistency across multiple runs of an algorithm: pairwise membership

Social networks are perhaps the purest example of “Web 2.0 ” services, and offer a sophisticated tool for accessing the preferences and properties of individuals and groups. Thus, they potentially allow up-to-date, richly annotated contextual data to be acquired as a side effect of users ’ everyday use of the services. In this paper, we explore how such “social sensing ” could be integrated into pervasive systems. We frame and survey the possible approaches to such an integration, and eventually discuss the open issues and challenges facing researchers. 1

Through measurements, researchers continue to produce large social graphs that capture relationships, transactions, and social interactions between users. Efficient analysis of these graphs requires algorithms that scale well with graph size. We examine node distance computation, a critical primitive in graph problems such as computing node separation, centrality computation, mutual friend detection, and community detection. For large million-node social graphs, computing even a single shortest path using traditional breadth-first-search can take several seconds. In this paper, we propose a novel node distance estimation mechanism that effectively maps nodes in high dimensional graphs to positions in low-dimension Euclidean coordinate spaces, thus allowing constant time node distance computation. We describe Orion, a prototype graph coordinate system, and explore critical decisions in its design. Finally, we evaluate the accuracy of Orion’s node distance estimates, and show that it can produce accurate results in applications such as node separation, node centrality, and ranked social search. 1

While most research in online social networks (OSNs) in the past has focused on static friendship networks, social network activity graphs are quite important as well. However, characterizing social network activity graphs is computationally intensive; reducing the size of these graphs using sampling algorithms is critical. There are two important requirements—the sampling algorithm must be able to preserve core graph characteristics and be amenable to a streaming implementation since activity graphs are naturally evolving in a streaming fashion. Existing approaches satisfy either one or the other requirement, but not both. In this paper, we propose a novel sampling algorithm called Streaming Time Node Sampling (STNS) that exploits temporal clustering often found in real social networks. Using real communication data collected from Facebook and Twitter, we show that STNS significantly out-performs stateof-the-art sampling mechanisms such as node sampling and Forest Fire sampling, across both averages and distributions of several graph properties.

Recent work on anonymizing online social networks (OSNs) has looked at privacy preserving techniques for publishing a single instance of the network. However, OSNs evolve and a single instance is inadequate for analyzing their evolution or performing longitudinal data analysis. We study the problem of repeatedly publishing OSN data as the network evolves while preserving privacy of users. Publishing multiple instances independently has privacy risks, since stitching the information together may allow an adversary to identify users. We provide methods to anonymize a dynamic network when new nodes and edges are added to the published network. These methods use link prediction algorithms to model the evolution. Using this predicted graph to perform group-based anonymization, the loss in privacy caused by new edges can be eliminated almost entirely. We propose metrics for privacy loss, and evaluate them for publishing multiple OSN instances. 1

Social networking is one of the most popular Internet activities with millions of members from around the world. However, users are unaware of the privacy risks involved. Even if they protect their private information, their name is enough to be used for malicious purposes. In this paper we demonstrate and evaluate how names extracted from social networks can be used to harvest email addresses as a first step for personalized phishing campaigns. Our blind harvesting technique uses names collected from the Facebook and Twitter networks as query terms for the Google search engine, and was able to harvest almost 9 million unique email addresses. We compare our technique with other harvesting methodologies, such as crawling the World Wide Web and dictionary attacks, and show that our approach is more scalable and efficient than the other techniques. We also present three targeted harvesting techniques that aim to collect email addresses coupled with personal information for the creation of personalized phishing emails. By using information available in Twitter to narrow down the search space and, by utilizing the Facebook email search functionality, we are able to successfully map 43.4 % of the user profiles to their actual email address. Furthermore, we harvest profiles from Google Buzz, 40 % of whom provide a direct mapping to valid Gmail addresses.

Different from Laplacian or normal matrix, the properties of the adjacency eigenspace received much less attention. Recent work showed that n-odes projected into the adjacency eigenspace exhibit an orthogonal line pattern and nodes from the same community locate along the same line. In this paper, we conduct theoretical studies based on graph perturbation to demonstrate why this line orthogonality property holds in the adjacency eigenspace and why it generally disappears in the Laplacian and normal eigenspaces. Using the orthogonality property in the adjacency eigenspace, we present a graph partition algorithm, AdjCluster, which first projects node coordinates to the unit sphere and then applies the classic k-means to find clusters. Empirical evaluations on synthetic data and real-world social networks validate our theoretical findings and show the effectiveness of our graph partition algorithm. 1

Abstract—Online Social Network (OSN) operators are interested in promoting usage among their users, and try a variety of strategies to encourage use. Some recruit celebrities to their site, some allow third parties to develop applications that run on their sites, and all have features intended to encourage use. As important as usage is, we are unaware of any studies into what influences users to be active and to remain online. This paper is the first work studying the lifetime of OSN users, examining the factors that influence lifetime in one OSN, Buzznet. The major contributions of this work are the study of active lifetime, the features and behaviors that encourage activity, and the comparison of active lifetime to passive lifetime. I.

Distributed hash tables suffer from several security and privacy vulnerabilities, including the problem of Sybil attacks. Existing social network-based solutions to mitigate the Sybil attacks in DHT routing have a high state requirement and do not provide an adequate level of privacy. For instance, such techniques require a user to reveal their social network contacts. We design X-Vine, a protection mechanism for distributed hash tables that operates entirely by communicating over social network links. As with traditional peer-topeer systems, X-Vine provides robustness, scalability, and a platform for innovation. The use of social network links for communication helps protect participant privacy and adds a new dimension of trust absent from previous designs. X-Vine is resilient to denial of service via Sybil attacks, and in fact is the first Sybil defense that requires only a logarithmic amount of state per node, making it suitable for large-scale and dynamic settings. X-Vine also helps protect the privacy of users social network contacts and keeps their IP addresses hidden from those outside of their social circle, providing a basis for pseudonymous communication. We first evaluate our design with analysis and simulations, using several real world large-scale social networking topologies. We show that the constraints of X-Vine allow the insertion of only a logarithmic number of Sybil identities per attack edge; we show this mitigates the impact of malicious attacks while not affecting the performance of honest nodes. Moreover, our algorithms are efficient, maintain low stretch, and avoid hot spots in the network. We validate our design with a Planetlab implementation and a Facebook plugin. 1.