2013/07/11 by Emanuele Massaro, Lorenzo Valerio, Massaro, Emanuele +7
Computer Science · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Human Mobility and Location-Based Analysis #Opinion Dynamics and Social Influence #Opportunistic and Delay-Tolerant Networks #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1307.3003
openalex publication_date 2013/07/11 · openalex created_date 2022/08/06 · openalex updated_date 2026/07/28
The emergence and the global adaptation of mobile devices has influenced\nhuman interactions at the individual, community, and social levels leading to\nthe so called Cyber-Physical World (CPW) convergence scenario [1]. One of the\nmost important features of CPW is the possibility of exploiting information\nabout the structure of the social communities of users, revealed by joint\nmovement patterns and frequency of physical co-location. Mobile devices of\nusers that belong to the same social community are likely to "see" each other\n(and thus be able to communicate through ad-hoc networking techniques) more\nfrequently and regularly than devices outside the community. In mobile\nopportunistic networks, this fact can be exploited, for example, to optimize\nnetworking operations such as forwarding and dissemination of messages. In this\npaper we present the application of a cognitive-inspired algorithm [2,3,4] for\nrevealing the structure of these dynamic social networks (simulated by the HCMM\nmodel [5]) using information about physical encounters logged by the users'\nmobile devices. The main features of our algorithm are: (i) the capacity of\ndetecting social communities induced by physical co-location of users through\ndistributed algorithms; (ii) the capacity to detect users belonging to more\ncommunities (thus acting as bridges across them), and (iii) the capacity to\ndetect the time evolution of communities.\n