Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 1) Detecting Special Individuals: <s> We introduce a novel frequent pattern mining approach to discover leaders and tribes in social networks. In particular, we consider social networks where users perform actions. Actions may be as simple as tagging resources (urls) as in del.icio.us, rating songs as in Yahoo! Music, or movies as in Yahoo! Movies, or users buying gadgets such as cameras, handhelds, etc. and blogging a review on the gadgets. The assumption is that actions performed by a user can be seen by their network friends. Users seeing their friends' actions are sometimes tempted to perform those actions. We are interested in the problem of studying the propagation of such "influence", and on this basis, identifying which users are leaders when it comes to setting the trend for performing various actions. We consider alternative definitions of leaders based on frequent patterns and develop algorithms for their efficient discovery. Our definitions are based on observing the way influence propagates in a time window, as the window is moved in time. Given a social graph and a table of user actions, our algorithms can discover leaders of various flavors by making one pass over the actions table. We run detailed experiments to evaluate the utility and scalability of our algorithms on real-life data. The results of our experiments confirm on the one hand, the efficiency of the proposed algorithm, and on the other hand, the effectiveness and relevance of the overall framework. To the best of our knowledge, this the first frequent pattern based approach to social network mining. <s> BIB001 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 1) Detecting Special Individuals: <s> Given a task T, a pool of individuals X with different skills, and a social network G that captures the compatibility among these individuals, we study the problem of finding X, a subset of X, to perform the task. We call this the T EAM F ORMATION problem. We require that members of X' not only meet the skill requirements of the task, but can also work effectively together as a team. We measure effectiveness using the communication cost incurred by the subgraph in G that only involves X'. We study two variants of the problem for two different communication-cost functions, and show that both variants are NP-hard. We explore their connections with existing combinatorial problems and give novel algorithms for their solution. To the best of our knowledge, this is the first work to consider the T EAM F ORMATION problem in the presence of a social network of individuals. Experiments on the DBLP dataset show that our framework works well in practice and gives useful and intuitive results. <s> BIB002 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 1) Detecting Special Individuals: <s> Network analysis offers many centrality measures that are successfully utilized in the process of investigating the social network profile. The most important and representative measures are presented in the paper. It includes indegree centrality, proximity prestige, rank prestige, node position, outdegree centrality, eccentrality, closeness centrality, and betweenes centrality. Both feature analysis and experimental comparative studies revealed the general profile of selected measures. <s> BIB003 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 1) Detecting Special Individuals: <s> In a blog network, there are special users who induce other users to actively utilize blog services. In this paper, these users whose contents exhibit large influence over other users are defined as Content Power Users (CPUs). Accurately determining who content power users are in a blog network is important in order to establish an effective business policy that stimulates usage of blog services. In this paper, we propose a new method of determining content power users. First, we measure the influence of each document owned by individual users. Since the influence of a document tends to be high with long exposure, we adjust the value of influence of a document based on exposure duration. Then, by adding up the values of influence of all the documents owned by each user, we calculate the influence of the corresponding user. We analyze the performance of our method, by applying the proposed method to an actual blog network and comparing its results to those of preexisting methods for determining power users. The experimental results demonstrate that our method is well suited for the dynamic nature of the blog network. <s> BIB004 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 1) Detecting Special Individuals: <s> Generating targeted advertisements for online social networks is a problem of growing interest. Monetizing activity in online social networks has been the topic of heated discussion lately. The undiscriminating tastes and spending power of a majority of its members makes this medium for self-expression and opinion sharing a very lucrative venue for advertising. The recent $240 million investment by Microsoft in Facebook clearly reaffirms the opportunity in targeted advertising for online social networks. Content-targeted advertisement programs such as Google AdSense and Yahoo Contextual Match work by automatically spotting keywords in web pages and displaying ads based on the keywords. The displayed ads are also refereed as Contextual Advertisements. These ads are generally represented by a URL along with a textual description that is also used to match the ad with the target page. This model works relatively well on the Web but its applicability to social networks is being met with some important challenges. User activity on venues such as forums, marketplaces and groups on social networking sites are excellent targets for monetization. We present an algorithm based on keyword clustering to generate targeted ads. <s> BIB005 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 1) Detecting Special Individuals: <s> 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. <s> BIB006 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 1) Detecting Special Individuals: <s> Twitter, a microblogging service less than three years old, commands more than 41 million users as of July 2009 and is growing fast. Twitter users tweet about any topic within the 140-character limit and follow others to receive their tweets. The goal of this paper is to study the topological characteristics of Twitter and its power as a new medium of information sharing. We have crawled the entire Twitter site and obtained 41.7 million user profiles, 1.47 billion social relations, 4,262 trending topics, and 106 million tweets. In its follower-following topology analysis we have found a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks [28]. In order to identify influentials on Twitter, we have ranked users by the number of followers and by PageRank and found two rankings to be similar. Ranking by retweets differs from the previous two rankings, indicating a gap in influence inferred from the number of followers and that from the popularity of one's tweets. We have analyzed the tweets of top trending topics and reported on their temporal behavior and user participation. We have classified the trending topics based on the active period and the tweets and show that the majority (over 85%) of topics are headline news or persistent news in nature. A closer look at retweets reveals that any retweeted tweet is to reach an average of 1,000 users no matter what the number of followers is of the original tweet. Once retweeted, a tweet gets retweeted almost instantly on next hops, signifying fast diffusion of information after the 1st retweet. To the best of our knowledge this work is the first quantitative study on the entire Twittersphere and information diffusion on it. <s> BIB007 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 1) Detecting Special Individuals: <s> We discuss the use of social networks in implementing viral marketing strategies. In the first part of this tutorial, we study influence maximization or how the structure of the social network affects the spread of behaviors and technologies. In the second part, we then consider how one might monopolize these natural processes to generate revenue in a revenue maximization setting. <s> BIB008 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 1) Detecting Special Individuals: <s> In this work we analyze the behavior on a company-internal social network site to determine which interaction patterns signal closeness between colleagues. Regression analysis suggests that employee behavior on social network sites (SNSs) reveals information about both professional and personal closeness. While some factors are predictive of general closeness (e.g. content recommendations), other factors signal that employees feel personal closeness towards their colleagues, but not professional closeness (e.g. mutual profile commenting). This analysis contributes to our understanding of how SNS behavior reflects relationship multiplexity: the multiple facets of our relationships with SNS connections. <s> BIB009
Some people with special characteristics may be attractive for some companies, manufacturers, organizations, etc. for example it may be desirable to find special persons with high skill in a special field or to find most influential persons in propagating some special kind of content. In  a social search engine named Aardvark (1 T U http://vark.comU 1 T ) is presented that needs to find the best person for answering a specific question, and one of its information resources is people's profiles on facebook. In BIB002 some work is done towards forming a team of experts from members of a social network. In BIB007 to specify influential persons within Twitter, ranking people based on their followers, PageRank and number of retweets is investigated. In BIB001 some definitions are defined for different people whose actions impacts on making the same actions by others and such people are called leaders. In this paper some algorithms are presented for detecting these people by the use of a social graph and a table which contains users' actions. In BIB003 some references are cited in which some methods for extracting most important (central) members are presented. It has mentioned strengths and weaknesses of some metrics. In BIB004 Content Power Users (CPUs) in blog networks are defined as users whose published content has a lot of impact on other users' actions. In this paper a method for identifying these users is presented and some other research works about detecting highly influential people in social networks are cited. 2) Commerce and Marketing: Advertisement in SNSs can be targeted BIB008 [31] BIB001 . Targeting users whose activity influences others could be beneficial for companies BIB001 . As is mentioned in BIB008 a manufacturer can select a number of users and give them its product with some discount or even free, and hope that their influence on other users promotes their product. In the case of discounting the amount of discount is exposed to discussion. In BIB005 using users' profile information and the information about their activities towards targeted advertisement is mentioned. 3) Monitoring and Analyzing Users' Activities: In BIB009 users' behavior in a social network is analyzed to identify the patterns of closeness between colleagues. Paper BIB006 notes the importance of awareness about users' participation patterns in knowledge-sharing social networks for researchers and social network industry; and analyses users' activities in three social networks. Some results that are different from common assumptions are reported.