Community detection in complex network based on an improved random algorithm using local and global network information

Dabaghi‐Zarandi, Fahimeh; KamaliPour, Parsa

doi:10.1016/j.jnca.2022.103492

Cited by 9 publications

(2 citation statements)

References 28 publications

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“…You et al [27] proposed a three-stage community discovery algorithm TS, which obtained good results through central node identification, label propagation, and community combination. Fahimeh et al [28] proposed a community detection algorithm that utilizes both local and global network information. The algorithm consists of four components: preprocessing, master community composition, community merging, and optimal community structure selection.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity

Huang,

2023

Applied Sciences

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Community detection is an important task in the analysis of complex networks, which is significant for mining and analyzing the organization and function of networks. As an unsupervised learning algorithm based on the particle competition mechanism, stochastic competitive learning has been applied in the field of community detection in complex networks, but still has several limitations. In order to improve the stability and accuracy of stochastic competitive learning and solve the problem of community detection, we propose an unsupervised community detection algorithm LNSSCL (Local Node Similarity-Integrated Stochastic Competitive Learning). The algorithm calculates node degree as well as Salton similarity metrics to determine the starting position of particle walk; local node similarity is incorporated into the particle preferential walk rule; the particle is dynamically adjusted to control capability increments according to the control range; particles select the node with the strongest control capability within the node to be resurrected; and the LNSSCL algorithm introduces a node affiliation selection step to adjust the node community labels. Experimental comparisons with 12 representative community detection algorithms on real network datasets and synthetic networks show that the LNSSCL algorithm is overall better than other compared algorithms in terms of standardized mutual information (NMI) and modularity (Q). The improvement effect for the stochastic competition learning algorithm is evident, and it can effectively accomplish the community detection task in complex networks.

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Section: Related Workmentioning

confidence: 99%

Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity

Huang,

2023

Applied Sciences

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“…We consider four real-world networks with diverse characteristics, namely Zachary's Karate Club, dolphin social network, American college football, and Polbooks network, all of which have been extensively studied in recent works [32][33][34]. Table 1 provides details about the four real-world networks.…”

Section: Experiments On Real Networkmentioning

confidence: 99%

A Two-Stage Multi-Objective Evolutionary Algorithm for Community Detection in Complex Networks

Zhu

Wei

2023

Mathematics

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Community detection is a crucial research direction in the analysis of complex networks and has been shown to be an NP-hard problem (a problem that is at least as hard as the hardest problems in nondeterministic polynomial time). Multi-objective evolutionary algorithms (MOEAs) have demonstrated promising performance in community detection. Given that distinct crossover operators are suitable for various stages of algorithm evolution, we propose a two-stage algorithm that uses an individual similarity parameter to divide the algorithm into two stages. We employ appropriate crossover operators for each stage to achieve optimal performance. Additionally, a repair operation is applied to boundary-independent nodes during the second phase of the algorithm, resulting in improved community partitioning results. We assessed the effectiveness of the algorithm by measuring its performance on a synthetic network and four real-world network datasets. Compared to four existing competing methods, our algorithm achieves better accuracy and stability.

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Community detection based on improved user interaction degree, weighted quasi-local path-based similarity and frequent pattern mining

Sayari,

Harounabadi,

Banirostam

2024

J Supercomput

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Community detection in complex network based on an improved random algorithm using local and global network information

Cited by 9 publications

References 28 publications

Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity

Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity

A Two-Stage Multi-Objective Evolutionary Algorithm for Community Detection in Complex Networks

Community detection based on improved user interaction degree, weighted quasi-local path-based similarity and frequent pattern mining

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