Improving the performance of evolutionary multi-objective co-clustering models for community detection in complex social networks
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Publication Date
Thu Dec 01 2016
Journal Name
Swarm And Evolutionary Computation
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Publication Date
Sun Dec 01 2019
Journal Name
Applied Soft Computing
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Publication Date
Sun Nov 01 2020
Journal Name
2020 2nd Annual International Conference On Information And Sciences (aicis)
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Publication Date
Sun Mar 01 2020
Journal Name
Computer Networks
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Publication Date
Wed Nov 17 2021
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
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Publication Date
Thu Jan 30 2025
Journal Name
Iraqi Journal Of Science
Improving the Reliability of Evolutionary Algorithm for Complex Detection in Noisy Protein-Protein Interaction Networks
Complex detection
Evolutionary algorithm
Missing PPI
Modularity
Protein–protein interaction
Unreliable PPI.
Evolutionary algorithms are better than heuristic algorithms at finding protein complexes in protein-protein interaction networks (PPINs). Many of these algorithms depend on their standard frameworks, which are based on topology. Further, many of these algorithms have been exclusively examined on networks with only reliable interaction data. The main objective of this paper is to extend the design of the canonical and topological-based evolutionary algorithms suggested in the literature to cope with noisy PPINs. The design of the evolutionary algorithm is extended based on the functional domain of the proteins rather than on the topological domain of the PPIN. The gene ontology annotation in each molecular function, biological proce
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Publication Date
Sat Sep 27 2014
Journal Name
Soft Computing
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Publication Date
Thu Oct 31 2019
Journal Name
Journal Of Theoretical And Applied Information Technology
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Publication Date
Sat Aug 25 2012
Journal Name
Wireless Personal Communications
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Publication Date
Thu Feb 09 2023
Journal Name
Artificial Intelligence Review
Community detection model for dynamic networks based on hidden Markov model and evolutionary algorithm
Finding communities of connected individuals in complex networks is challenging, yet crucial for understanding different real-world societies and their interactions. Recently attention has turned to discover the dynamics of such communities. However, detecting accurate community structures that evolve over time adds additional challenges. Almost all the state-of-the-art algorithms are designed based on seemingly the same principle while treating the problem as a coupled optimization model to simultaneously identify community structures and their evolution over time. Unlike all these studies, the current work aims to individually consider this three measures, i.e. intra-community score, inter-community score, and evolution of community over
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