Hybrid Bridge-Based Memetic Algorithms for Finding Bottlenecks in Complex Networks

Chalupa, David; Hawick, Ken; Walker, James

Abstract

We propose a memetic approach to find bottlenecks in complex networks based on searching for a graph partitioning with minimum conductance. Finding the optimum of this problem, also known in statistical mechanics as the Cheeger constant, is one of the most interesting NP-hard network optimisation problems. The existence of low conductance minima indicates bottlenecks in complex networks. However, the problem has not yet been explored in depth in the context of applied discrete optimisation and evolu- tionary approaches to solve it. In this paper, the use of a memetic frame- work is explored to solve the minimum condutance problem. The approach combines a hybrid method of initial population generation based on bridge identification and local optima sampling with a steady-state evolutionary process with two local search subroutines. These two local search subrou- tines have complementary qualities. Efficiency of three crossover operators is explored, namely one-point crossover, uniform crossover, and our own par- tition crossover. Experimental results are presented for both artificial and real-world complex networks. Results for Barab ́asi-Albert model of scale-free networks are presented, as well as results for samples of social networks and protein-protein interaction networks. These indicate that both well-informed initial population generation and the use of a crossover seem beneficial in solving the problem in large-scale.

Citation

Chalupa, D., Hawick, K., & Walker, J. (2018). Hybrid Bridge-Based Memetic Algorithms for Finding Bottlenecks in Complex Networks. Big Data Research, 14, 68-80. https://doi.org/10.1016/j.bdr.2018.04.001

• Journal Article Type: Article
• Acceptance Date: Apr 8, 2018
• Online Publication Date: Apr 16, 2018
• Publication Date: 2018-12
• Deposit Date: Jun 13, 2018
• Publicly Available Date: Apr 17, 2019
• Electronic ISSN: 2214-5796
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Pages: 68-80
• Series Title: Big Data Research
• DOI: https://doi.org/10.1016/j.bdr.2018.04.001
• Keywords: Memetic algorithms; Bottlenecks; Complex networks; Minimum conductance problem; Sparsest cut; Cheeger constant
• Public URL: https://hull-repository.worktribe.com/output/875075
• Publisher URL: https://www.journals.elsevier.com/big-data-research
• Contract Date: Jun 13, 2018