Social Networks Research Aspects: A Vast and Fast Survey Focused on the Issue of Privacy in Social Network Sites <s> 14) Prediction: <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> 14) Prediction: <s> Prediction of links - both new as well as recurring - in a social network representing interactions between individuals is an important problem. In the recent years, there is significant interest in methods that use only the graph structure to make predictions. However, most of them consider a single snapshot of the network as the input, neglecting an important aspect of these social networks viz., their evolution over time. In this work, we investigate the value of incorporating the history information available on the interactions (or links) of the current social network state. Our results unequivocally show that time-stamps of past interactions significantly improve the prediction accuracy of new and recurrent links over rather sophisticated methods proposed recently. Furthermore, we introduce a novel testing method which reflects the application of link prediction better than previous approaches. <s> BIB002
By studying the information of SNSs it is possible to predict some events that may happen in future. For example some research has been done recently to predict future links in social networks BIB001 . In BIB002 some techniques for predicting the links that may be established in future are presented.