Investigating Facebook groups through a random graph model

Facebook disseminates messages for billions of users everyday. Though there are log files stored on central servers, law enforcement agencies outside of the U.S. cannot easily acquire server log files from Facebook. This work models Facebook user groups by using a random graph model. Our aim is to facilitate detectives quickly estimating the size of a Facebook group with which a suspect is involved. We estimate this group size according to the number of immediate friends and the number of extended friends which are usually accessible by the public. We plot and examine UML diagrams to describe Facebook functions. Our experimental results show that asymmetric Facebook friendship fulfills the assumption of applying random graph models.