Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 6) Social Network Extraction: <s> In the paper, we present an approach to mining a directed social network from a message board on the Internet where vertices denote individuals and directed links denote the flow of influence. The influence is measured based on propagating terms among individuals via messages. The distance with respect to contextual similarity between individuals is acquired since the influence indicates the degree of their shared interest represented as terms. <s> BIB001 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 6) Social Network Extraction: <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> BIB002
There are a lot of data in various forms on the web that apparently do not have the structure of a social network but with some mining activities on them like extracting the identity of data owners and the relations between them it is possible to extract the social network that relies beneath these data. Examples of such studies are BIB001 and  . In BIB001 the information of a message board is used and in  a system named ArnetMiner [http://www.arnetminer.org] is presented that extracts a social network of researchers. In BIB002 extraction of social network using a blog is mentioned.