A multiobjective evolutionary algorithm for optimizing the small-world property

Small-world effect plays an important role in the field of network science, and optimizing the small-world property has been a focus, which has many applications in computational social science. In the present study, we model the problem of optimizing small-world property as a multiobjective optimiz...

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Vydáno v:PloS one Ročník 19; číslo 12; s. e0313757
Hlavní autoři: Zhang, Ruochen, Zhu, Bin
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 03.12.2024
Public Library of Science (PLoS)
Témata:
Algorithms
Analysis
Biology and Life Sciences
Cluster Analysis
Clustering
Community structure
Computer and Information Sciences
Computer Simulation
Ecology and Environmental Sciences
Efficiency
Evolutionary algorithms
Genetic algorithms
Humans
Information management
Interdisciplinary subjects
Job hunting
Models, Theoretical
Multiple objective analysis
Optimization
Pareto optimization
Pareto optimum
Physical Sciences
Research and Analysis Methods
Social networks
Social organization
Social sciences
China
ISSN:1932-6203, 1932-6203
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Shrnutí:Small-world effect plays an important role in the field of network science, and optimizing the small-world property has been a focus, which has many applications in computational social science. In the present study, we model the problem of optimizing small-world property as a multiobjective optimization, where the average clustering coefficient and average path length are optimized separately and simultaneously. A novel method for optimizing small-world property is then proposed based on the multiobjective evolutionary algorithm with decomposition. Experimental results have proved that the presented method is capable of solving this problem efficiently, where a uniform distribution of solutions on the Pareto-optional front can be generated. The optimization results are further discussed to find specific paths for optimizing different objective functions. In general, adding edges within the same community is helpful for promoting ACC, while adding edges between different communities is beneficial for reducing APL. The optimization on networks with the feature of community structure is more remarkable, but community structure has less impact on the optimization when the internal community is triangles-saturated.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0313757