Numerical Optimization Algorithms for Optimizing Node Centrality in Complex Networks

Abstract

Centrality measures play a crucial role in various fields, including social network analysis, transportation network optimization, and information flow analysis. In this paper, we present a new approach to optimize network centrality. We developed an optimization model to increase the centrality of nodes in the network by using numerical optimization procedures. To evaluate the proposed approach two well-known algorithms, SLSQP and COBYLA, implemented in maximizing centrality through extensive tests on a random network. The outcomes show the value of our suggested strategy in enhancing network centrality and suggest the possibility of outperforming more conventional methods. Finally, python language puts the strategy into practice and produces the desired outcomes