Utilizing Social Influence in Content Distribution Networks

Online social networks (OSNs) provide new means of disseminating information about applications and contents served by network providers. OSN members often reveal their usage information and opinion about applications and contents to their neighbors within their social networks. Consequently, sudden popularity and viral propagation of applications among OSN members can put significant burden on network resources and degrade network performance. Further, viral exchanges might propagate malicious applications and such propagation might need to be kept in check. Accordingly, we propose a novel content distribution architecture that controls the resource utilization within an operator´s network by utilizing the existing social connections between users and building a model of information diffusion within the social network. Our method is based upon computing a reward function that takes into account the influence of users over each other in a given social network in terms of application adoption and content consumption. Based on this reward function and assuming that not only the users but also the network operator can limit the exposure of application usage and content consumption over the online social networks, we present algorithms to slow down or speed up the adoption/consumption of different applications/contents given the current state of both the physical network and the social network. We evaluate the effectiveness of this method over a Flickr data set. Results suggest that such a control is indeed possible and the proposed method significantly outperforms other approaches that employs mainly degree-based mechanisms for controlling the information dissemination over the social graphs.