| Abstract |
Pervasive systems such as Bluetooth-based infrastructures are capable of detecting close proximity and social connections. As a result, they are increasingly used for deriving social networks. However, the validity and reliability of thereby derived networks is questionable as evaluation procedures are often omitted. In this paper, we consider the process of deriving and evaluating a Bluetooth derived network as a parameter optimization problem. We investigate the effect of the number of detected connections, time window in which these are detected, and the direction of the resulting connection. Our results confirm the importance of conducting a throughout evaluation procedure when deriving social networks based on Bluetooth data. Going through the parameter optimization process, we are able to increase the accuracy of the derived BT networks by maximum of 10%, compared to deriving the networks without it. Our outcomes indicate that reducing the false positives can be achieved by setting a particular weight threshold. Furthermore, with the window size parameter we show that more BT observations does not necessarily mean more accurate networks. Finally, with respect to the edge type, we observe the the accuracy of deriving undirected networks is higher than the accuracy of deriving directed networks. |