Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 8) Link and Relationship Mining: <s> Social networks can be used to model social interactions between individuals. In many circumstances, not all interactions between individuals are observed. In such cases, a social network is constructed with the data that has been observed, as this is the best one can do. Recent research has attempted to predict future links in a social network, though this has proven a very challenging task. Rather than predicting future links, we propose an inference method for recovering the links in a social network that already exist but that have not been observed. In addition, our approach automatically identifies groups of individuals that form tight-knit communities and models the intra and inter-community interactions. At this higher level of abstraction and for a social network built frommobile phone calls, our method is able to accurately identify a subset of 10% of all community pairs where about 50% of the pairs have had unobserved communication between them, an improvement of about four times over a subset of the same size with randomly chosen pairs. To the best of our knowledge, this is the first method that infers links that exist but are unobservable in a phone call-based social network. In addition, we perform the inference at the community level, where the discovery of unobserved inter-community communication can provide further insight into the organizational structure of the social network andcan identify social groups that may share common interests. <s> BIB001 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 8) Link and Relationship Mining: <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> BIB002 </s> Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 8) Link and Relationship Mining: <s> Previous work analyzing social networks has mainly focused on binary friendship relations. However, in online social networks the low cost of link formation can lead to networks with heterogeneous relationship strengths (e.g., acquaintances and best friends mixed together). In this case, the binary friendship indicator provides only a coarse representation of relationship information. In this work, we develop an unsupervised model to estimate relationship strength from interaction activity (e.g., communication, tagging) and user similarity. More specifically, we formulate a link-based latent variable model, along with a coordinate ascent optimization procedure for the inference. We evaluate our approach on real-world data from Facebook and LinkedIn, showing that the estimated link weights result in higher autocorrelation and lead to improved classification accuracy. <s> BIB003
In BIB002 relationship closeness is investigated based on the behavior of users in a social network inside a company. It is mentioned that Some behaviors are a sign of professional closeness and some are a sign of personal closeness. In BIB003 an approach is presented for estimation of relationship strength, and is evaluated with facebook and linkedin data. In BIB001 an approach for inferring the links that exist but are not observed is presented and a good survey about link mining in social networks is cited.