Determining the best algorithm to detect community structures in networks: application to power systems

Claudio M. Rocco, Kash Barker () and Jose Moronta
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Claudio M. Rocco: Universidad Central de Venezuela
Kash Barker: University of Oklahoma
Jose Moronta: Universidad Simón Bolívar

Environment Systems and Decisions, 2022, vol. 42, issue 2, 251-264

Abstract: Abstract A common feature of many networks is the presence of communities, or groups of relatively densely connected nodes with sparse connections between groups. An understanding of community structures could enable the network design for improved system performance. For electric power systems, most work in the detection of community structures (i) selects a specific algorithm to perform the detection of communities (or compares a proposed algorithm against algorithms), and (ii) focuses on topological information about the networks. The objective of this article is to provide a framework to improve the selection of appropriate community detection algorithms for a family of networks with similar structures. We propose an approach to determine the most effective community detection algorithm for a set of networks and compare which algorithms provide the most similar partitions across these networks. To illustrate the comparison of various community detection algorithms, 16 electric power systems are analyzed.

Keywords: Community detection algorithms; Multi-criteria decision analysis; Power systems; Rank aggregation; TOPSIS; Sensitivity analysis (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10669-021-09833-z

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