An efficient algorithm for community detection in complex weighted networks

Masooleh, Leila Samandari; Arbogast, Jeffrey E.; Seider, Warren D.; Oktem, Ulku; Soroush, Masoud

doi:10.1002/aic.17205

Cited by 5 publications

(2 citation statements)

References 70 publications

(86 reference statements)

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“…Generally, community detection problems have been solved using metaheuristic optimization methods due to their intractable computations and complexity where they have been categorized as highly combinatorial optimization NP-hard problems [18,19], and because solving it takes a long time form the CPU. most of the current metaheuristic methods suffer from slow convergence when applied to solve large-scale problems [20]. Therefore, this work improves the performance of the PSO in terms of speed by exploiting the inherently parallel nature of the metaheuristic algorithm and implementing parallel computing to execute the proposed MP-PSO algorithm.…”

Section: Proposed Methodsmentioning

confidence: 99%

Parallel Particle Swarm Optimization Algorithm for Identifying Complex Communities in Biological Networks

Abduljabbar

2024

Iraqi Journal of Science

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Identification of complex communities in biological networks is a critical and ongoing challenge since lots of network-related problems correspond to the subgraph isomorphism problem known in the literature as NP-hard. Several optimization algorithms have been dedicated and applied to solve this problem. The main challenge regarding the application of optimization algorithms, specifically to handle large-scale complex networks, is their relatively long execution time. Thus, this paper proposes a parallel extension of the PSO algorithm to detect communities in complex biological networks. The main contribution of this study is summarized in three- fold; Firstly, a modified PSO algorithm with a local search operator is proposed to detect complex biological communities with high quality. Secondly, the variability in the capability of PSO to extract community structure in biological networks is studied when different types of crossover operators are used. Finally, to reduce the computational time needed to solve this problem, especially when detecting complex communities in large-scale biological networks, we have implemented parallel computing to execute the algorithm. The performance of the proposed algorithm was tested and evaluated on two real biological networks. The experimental results showed the effective performance of the proposed algorithm when using single-point crossover operator, and its superiority over other counterpart algorithms. Moreover, the use of parallel computing in the proposed algorithm representation has greatly reduced the computational time required for its execution.

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Section: Proposed Methodsmentioning

confidence: 99%

Parallel Particle Swarm Optimization Algorithm for Identifying Complex Communities in Biological Networks

Abduljabbar

2024

Iraqi Journal of Science

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“…To decompose a large‐scale chemical process into observable subsystems, we use our method presented in Refs. [23, 24]. The method solves an optimization problem using an efficient whale optimization algorithm 23 .…”

Section: Nonlinear Distributed State Estimation With Delayed Measurem...mentioning

confidence: 99%

Distributed state estimation from infrequent and delayed measurements in large‐scale processes

2023

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Effective control and monitoring of a process usually require frequent and delay-free measurements of important process output variables. However, these measurements are often either not available or available infrequently with significant time delays. This article presents a method that allows for improving the performance of distributed state estimators implemented on large-scale manufacturing processes. The method uses a sample state augmentation approach that permits using delayed measurements in distributed state estimation. The method can be used with any state estimator, including unscented Kalman filters, extended Kalman filters, and moving horizon state estimators. The method optimally handles the tradeoff between computational time and estimation accuracy in distributed state estimation implemented using a computer with parallel processors. Its implementation and performance are shown using a few simulated examples.

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Causal network inference and functional decomposition for decentralized statistical process monitoring: Detection and diagnosis

Paredes

Rato

Reis

2023

Chemical Engineering Science

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An efficient algorithm for community detection in complex weighted networks

Cited by 5 publications

References 70 publications

Parallel Particle Swarm Optimization Algorithm for Identifying Complex Communities in Biological Networks

Parallel Particle Swarm Optimization Algorithm for Identifying Complex Communities in Biological Networks

Distributed state estimation from infrequent and delayed measurements in large‐scale processes

Causal network inference and functional decomposition for decentralized statistical process monitoring: Detection and diagnosis

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