This thesis develops a method which predicts the role of a node in a social network. For illustrative purposes the network used is a subset of Al-Qaeda from 1998 which contains a total of 160 members.
While doing exploratory analysis on this network we noticed that there seemed to be an underlying connection with the distance between two members and their roles. This led to developing a prediction method which could exploit this correlation structure. We use the geostatistical prediction method called Kriging that is modified to preform in a network; which we call Network Kriging.
This thesis gives the background knowledge necessary to understand the techniques, shows the results of Network Kriging and compares results to those using the K-Nearest Neighbours algorithm. We found that for important roles, such as Emir (Leadership), Network Kriging performs better than K-Nearest Neighbours.