The changing usage of a mature campus-wide wireless network
Proceedings of the 10th annual international conference on Mobile computing and networking
MSWiM '05 Proceedings of the 8th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Multicasting in delay tolerant networks: a social network perspective
Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing
Exploiting social interactions in mobile systems
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Peoplerank: social opportunistic forwarding
INFOCOM'10 Proceedings of the 29th conference on Information communications
Proceedings of the sixteenth annual international conference on Mobile computing and networking
The smallville effect: social ties make mobile networks more secure against node capture attack
Proceedings of the 8th ACM international workshop on Mobility management and wireless access
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Hi-index | 0.00 |
In this paper, we argue that the ability to accurately spot random and social relationships in dynamic networks is essential to network applications that rely on human routines, such as, e.g., opportunistic routing. We thus propose a strategy to analyze users' interactions in mobile networks where users act according to their interests and activity dynamics. Our strategy, named Random rElationship ClASsifier sTrategy (RECAST), allows classifying users' wireless interactions, separating random interactions from different kinds of social ties. To that end, RECAST observes how the real system differs from an equivalent one where entities' decisions are completely random. We evaluate the effectiveness of the RECAST classification on real-world user contact datasets collected in diverse networking contexts. Our analysis unveils significant differences among the dynamics of users' wireless interactions in the datasets, which we leverage to unveil the impact of social ties on opportunistic routing.