Abstract
BackgroundThis work is aimed at studying realistic social control strategies for social networks based on the introduction of random information into the state of selected driver agents. Deliberately exposing selected agents to random information is a technique already experimented in recommender systems or search engines, and represents one of the few options for influencing the behavior of a social context that could be accepted as ethical, could be fully disclosed to members, and does not involve the use of force or of deception.MethodsOur research is based on a model of knowledge diffusion applied to a time-varying adaptive network and considers two well-known strategies for influencing social contexts: One is the selection of few influencers for manipulating their actions in order to drive the whole network to a certain behavior; the other, instead, drives the network behavior acting on the state of a large subset of ordinary, scarcely influencing users. The two approaches have been studied in terms of network and diffusion effects. The network effect is analyzed through the changes induced on network average degree and clustering coefficient, while the diffusion effect is based on two ad hoc metrics which are defined to measure the degree of knowledge diffusion and skill level, as well as the polarization of agent interests.ResultsThe results, obtained through simulations on synthetic networks, show a rich dynamics and strong effects on the communication structure and on the distribution of knowledge and skills.ConclusionsThese findings support our hypothesis that the strategic use of random information could represent a realistic approach to social network controllability, and that with both strategies, in principle, the control effect could be remarkable.
Highlights
This work is aimed at studying realistic social control strategies for social networks based on the introduction of random information into the state of selected driver agents
The dynamically evolving topology changes neighborhood relations among nodes. It is in this context that we have studied the practical limitations affecting the selection of the theoretically best suited driver nodes—as we will discuss, under certain circumstances it is possible that the only realistic strategy is the opposite of what structural/pinning controllability theory would suggest—and the type of actions that could be realistically performed in a social context for controlling an adaptive network
We have considered the effects of random information enlarging the knowledge base of driver nodes as a control strategy
Summary
This work is aimed at studying realistic social control strategies for social networks based on the introduction of random information into the state of selected driver agents. The property of structural controllability is central in the study of how the dynamics of a complex system can be controlled [3]. From control theory, a dynamic system is said to exhibit structural controllability if, with a suitable selection of inputs, it can be driven from one state to any other state in finite time. Liu et al in a seminal paper demonstrated how the problem of determining the minimum set of driver nodes required for structural controllability can be mapped into a maximum matching problem [4]. Some remarkable theoretical results have been recently demonstrated for complex networks [5,6,7,8]
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