A Controllability Algorithm to Minimize the Spreading chance of COVID-19 in Individual Networks

Abstract

After the spread of the coronavirus. It became necessary to presenting artificial intelligence algorithms to study social contact between people. On the other hand, social network considers as a complex network. The situation became urgent to reduce these networks and reduce links between people inside each network while maintaining full controllability of the networks to reduce the number of real contact and minimize the cost of the networks especially in these bad days, which the world lives in due to the spread of epidemics, viruses, and infection. This paper aims to present a model that computes controllability on real contact people's networks to reduce touches and keep the network in a controlled manner based on three ways. a structural controllability approach is using by applying the Bipartitegraph algorithm and the Hopcroft-Karp algorithm. In order to determine the drive nodes that must be controlled to gain full control of the network, normal driver nodes and weak driver nodes. Weak driver nodes can remove to enhance controllability. As a result, network controllability increased by 12.8%, reduced rate of drive nodes, reduced the chance of spread of infection by 67%.