Large Scale Measurement and Analytics on Social Groups of Device-to-Device Sharing in Mobile Social Networks

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 large-scale measurement and analytics on D2D-based content sharing groups from the perspective of social networks via the platform of Xender, one of leading global D2D sharing platforms in Asia. This D2D sharing data is a precious offline social relationship that reflects the characteristics of large-scale offline users. We analyze the behaviors of about 40 million users with 443 million D2D transmissions of 17 million files in 865 thousand social groups, and unveil the details of social structure properties, network motifs, cascade trees of friendship and propagation which are helpful for improving social D2D sharing services.