Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 11) Information and Activity Diffusion and Propagation: <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> 11) Information and Activity Diffusion and Propagation: <s> In the blog network, the posts in a blog can be diffused to other blogs through trackbacks and scraps. Analyzing information diffusion in the blog network is an important research issue that can be used for predicting information diffusion, detecting abnormality, marketing, and revitalizing the blog world. Existing studies on information diffusion in a blog network define explicit relationships between blogs and analyze the word-of-mouth effect through such explicit relationships only. However, it has been observed that more than 85% of all information diffusion in a blog network occurs through non-explicit relationships. In this paper, we propose a new model that considers both the explicit and non-explicit relationships between blogs in order to explain the information diffusion phenomena in a blog network. We add a super node and the relationships between the super node and blogs as broadcast edges and register edges to the existing information diffusion model and assign the assimilation probability to every relationship. The expanded information diffusion model improves the accuracy of the basic model by taking into account the degrees of diffusion powers of posts. We verify the superiority of the proposed model through extensive experiments of information diffusion at a real blog network. The experimental results show that our expanded information diffusion model generates 77% less errors than the existing model. <s> BIB002
The way that information and activities propagate through a social network is another area that is worth to investigate and studying about it can have various benefits. For example commercial companies may be interested in the results of such studies to improve the spread of information about their products, or educational and cultural activists may benefit from it for promoting their goals. In BIB002 information propagation in blogs is studied and some applications for such a study are mentioned. In BIB001 the spread and prevalence of users' actions is investigated over time, to identify users whose actions have influence on other users' actions.