Abstract This chapter addresses two important issues in social network analysis that involve uncertainty. Firstly, we present an analysis on the robustness of centrality measures that extends the work presented in Borgatti et al. using three types of complex network structures and one real social network. Secondly, we present a method to predict edges in dynamic social networks. Our experimental results indicate that the robustness of the centrality measures applied to more realistic social networks follows a predictable pattern and that the use of temporal statistics could improve the accuracy achieved on edge prediction.
In REF , the authors examined the effects of different sampling techniques (selecting the nodes and links for addition/deletion) on the different types of complex networks with regards to the robustness of the centrality measures.
Centrality Robustness and Link Prediction in Complex Social Networks
Computational Social Networks
Computational Social Networks
Computer Science