  The emergence and the global adaptation of mobile devices has influenced
human interactions at the individual, community, and social levels leading to
the so called Cyber-Physical World (CPW) convergence scenario [1]. One of the
most important features of CPW is the possibility of exploiting information
about the structure of the social communities of users, revealed by joint
movement patterns and frequency of physical co-location. Mobile devices of
users that belong to the same social community are likely to "see" each other
(and thus be able to communicate through ad-hoc networking techniques) more
frequently and regularly than devices outside the community. In mobile
opportunistic networks, this fact can be exploited, for example, to optimize
networking operations such as forwarding and dissemination of messages. In this
paper we present the application of a cognitive-inspired algorithm [2,3,4] for
revealing the structure of these dynamic social networks (simulated by the HCMM
model [5]) using information about physical encounters logged by the users'
mobile devices. The main features of our algorithm are: (i) the capacity of
detecting social communities induced by physical co-location of users through
distributed algorithms; (ii) the capacity to detect users belonging to more
communities (thus acting as bridges across them), and (iii) the capacity to
detect the time evolution of communities.
