Analysis and Forecasting of States of Industrial Networks with Adaptive Topology Based on Network Motifs

Abstract

This article proposes an approach to study states of complex industrial networks with adaptive topology based on network motifs: statistically significant subgraphs of a larger graph. The presented analysis concerns the applicability of network motifs to characterizing the system’s performance and for short-, medium-, and long-term forecasting of system states. A smart grid network structure is used as an example: it is represented as a directed graph, in which the most frequent motifs are identified; several scenarios of attacks on network nodes are modeled, and a forecast of the network state is compiled. The results of experimental studies demonstrate the accuracy and consistency of the application of this mathematical tool to the considered problems.