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An Efficient Algorithm for Community Detection in Complex Weighted Networks
  • +2
  • Leila Masooleh,
  • Jeffrey Arbogast,
  • Warren Seider,
  • Ulku Oktem,
  • Masoud Soroush
Leila Masooleh
Drexel University
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Jeffrey Arbogast
American Air Liquide
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Warren Seider
University of Pennsylvania
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Ulku Oktem
Wharton School, University of Pennsylvania
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Masoud Soroush
Drexel University
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Abstract

Community detection decomposes large-scale, complex networks ‘optimally’ into sets of smaller sub-networks. It finds sub-networks that have the least inter-connections and the most intra-connections. This article presents an efficient community detection algorithm that detects community structures in a weighted network by solving a multi-objective optimization problem. The whale optimization algorithm is extended to enabe it to handle multi-objective optimization problems with discrete variables and to solve the problems on parallel processors. To this end, the population’s positions are discretized using a transfer function that maps real variables to discrete variables, the initialization steps for the algorithm are modified to prevent generating unrealistic connections between variables, and the updating step of the algorithm is redefined to produce integer numbers. To identify the community configurations that are Pareto optimal, the non-dominated sorting concept is adopted. The proposed algorithm is tested on the Tennessee Eastman process to show its application and performance.

Peer review status:Published

02 Aug 2020Submitted to AIChE Journal
06 Aug 2020Submission Checks Completed
06 Aug 2020Assigned to Editor
12 Aug 2020Reviewer(s) Assigned
30 Sep 2020Editorial Decision: Revise Major
10 Nov 20201st Revision Received
10 Nov 2020Submission Checks Completed
10 Nov 2020Assigned to Editor
13 Nov 2020Reviewer(s) Assigned
17 Dec 2020Editorial Decision: Revise Minor
21 Dec 20202nd Revision Received
29 Dec 2020Submission Checks Completed
29 Dec 2020Assigned to Editor
10 Jan 2021Editorial Decision: Accept
22 Jan 2021Published in AIChE Journal. 10.1002/aic.17205