Evolution Matters: Content Transmission in Evolving Wireless Social Networks

With the popularization of smart devices and social platforms (Facebook, Instagram and etc.), content transmission is becoming an increasingly prevalent form of human interaction with transmission time being a critical issue in miscellaneous applications. Extensive works have been devoted to this problem with the typical assumption that the network is non-evolving. In contrast, real-world networks which the transmission runs through are widely observed to be evolving over time and thus display distinctive properties. In this paper, we make the first study of content transmission in evolving networks. Particularly, we focus on the specific transmission time of the content, which is an important performance metric. For a comprehensive analysis, we consider both static and mobile cases. The network evolves in the following sense: each new user randomly chooses a geographic location while establishing social relations with existing users according to the evolving model, called Affiliation Networks. The transmission scheme running on the network exploits both social relationships and geographic information. While revealing the microscopic property of evolving social networks as the basis, in both static and mobile cases we manage to bound the transmission time in evolving networks which departs from results in non-evolving networks. In both cases, we find that the transmission time in evolving networks is smaller than the non-evolving counterpart under our scheme, and the gap is constantly increasing over time. Especially, the mobile case obtains such gain with less geographic knowledge. The theoretical findings are confirmed by experiments on both synthetic and real networks under different settings. We find that transmission time in evolving networks is smaller than non-evolving counterparts under the tested settings, and our proposed algorithms outperform the baselines.