TR-16-01: Towards Distribution-Based Control of Social Networks

Abstract

Complex networks are found in many domains and the control of these networks is a research topic that continues to draw increasing attention. This paper proposes a method of network control that attempts to maintain a specified target distribution of the network state. In contrast to many existing network control research works, which focus exclusively on structural analysis, this paper also involves the behavioural aspect of network control. The general architecture of a distribution-based control system is described and a distribution-based failure avoidance problem is formulated. A control system is proposed to control the state distribution of the real-valued voter model, which could have applications in problems such as the avoidance of consensus or extremism. The preliminary results presented in this paper demonstrate that a standard reinforcement learning approach is capable of learning a control signal selection policy to prevent the network state distribution from straying far from a specified target distribution. Several interesting questions arise from these results and are discussed as potential future work.

TR-16-01.pdf