Measurement and analytics on social groups of device-to-device sharing in mobile social networks

Abstract

Recently many studies demonstrate that exploiting the Device-to-Device (D2D) content sharing in offline Mobile Social Networks (MSNs) is a promising solution to offload cellular data to local connectivities in proximity to reduce the duplicated cellular transmissions via the backbone network demanded by nearby users and hence to improve users' quality of service (QoS). However, related D2D-based social sharing and offloading proposals are based on either assumptions and theoretical models, or limited data-driven analysis caused by small scale of data sets (e.g. hundreds of MSN users) or single-dimensional feature (e.g. human mobility only), which severely restricts applications in practice. In this paper, we perform the world-first large-scale measurement and analytics on D2D-based content sharing groups from the perspective of social networks via the platform of Xender, a leading global D2D sharing platform in Asia. We analyze the behaviours of about 30 million users with 443 million D2D transmissions of 17 million files in 884 thousand social groups, and unveil the details of social structure properties, network motifs, cascade trees of friendship and propagation, which are helpful for improving the service of social D2D sharing. Finally we discuss challenges and opportunities for improving social D2D sharing services.