(176b) Community Detection Based on Dynamic Attributes of Complex Networks

Real-world networks are complex and large scale, represent very interacting entities, and generate diverse types of data. Biological networks [1], power networks [2], internet networks, and chemical plants [3, 4] are examples of complex networks. Moreover, modern manufacturing plants are increasingly integrated [4], leading to structural and computational complexities. In recent years, the problem of decomposing a complex network into a set of interacting small networks that adequately capture the interactions of the original large network has received great attention, as the decomposition has applications in many engineering and science fields. Community detection has been used to decompose large networks into a set of small interacting networks [1]. Generally, it exploits this structure via optimization to find sub-networks that have the least inter-connections and the most intra-connections. Recently, several methods and algorithms have been proposed [5]; among them, modularity optimization is the most popular one [6].

In this work, we present and compare community detection methods that decompose a system into smaller subsystems based on different dynamic and steady-state attributes of the system. The dynamic attributes include response times and time delays of one variable with respect to another, and the steady-state ones include sensitivity of one variable to another at steady-state conditions. The application and performance of the methods are shown and compared by applying them to the Tennessee Eastman process. A multi-objective whale optimization algorithm is used to solve the community detection problems. The algorithm uses a nondominated sorting approach to calculate all non-dominated solutions.

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