Optimizing Mobile Radio Access Network Spectrum Refarming Using Community Detection Algorithms

Budic, D.; Skračić, Kristian; Bodrusic, I.

doi:10.23919/mipro.2019.8757081

Cited by 1 publication

(2 citation statements)

References 21 publications

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“…Research has been carried out and developed in various fields over the last decade. Studies such as the ones conducted by Budic et al [2] use GN to detect complex radio access network (RAN) communities, Yadav et al [10] for social network analysis, and Saputri et al [11] for student behavior based on students' browsing habits who use wi-fi at the university under study. Furthermore, studies employing GN are conducted, such as [12,13].…”

Section: Related Workmentioning

confidence: 99%

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GN-PPN: Parallel Girvan-Newman-Based Algorithm to Detect Communities in Graph with Positive and Negative Weights

2022

IJIES

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The Girvan-Newman (GN) method is one of the most popular methods for detecting communities. However, the method is applied to graphs with only positive weights, while graphs with positive and negative weights are in the real world. This paper proposes improving the GN method to work on graphs with positive and negative weights. The method is called Girvan-Newman Parallel Positive Negative (GN-PPN). Other than the benefit related to the use of negative weights, GN-PPN has a parallelization process that accelerates processing time. This method also uses edge betweenness and pair dependencies as calculations. The GN-PPN method is evaluated using modularity score, Normalized Mutual Information (NMI), and processing time. Our experiments show that the modularity score of the GN-PPN is similar to GN, modified GN (mGN), and Parallel GN (PGN). The accuracy of the GN-PPN method in detecting communities evaluated by NMI shows that the GN-PPN is better than similar methods such as Infomap, which ranges from 0.651 to 0.937. The processing time of the GN-PPN method shows a significant acceleration, which is a decrease of around 45% to 95% compared to other methods such as GN, mGN, and PGN.

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Section: Related Workmentioning

confidence: 99%

“…Furthermore, the GN method also introduces modularity, which measures the quality of the formed community. Many researchers have proved the GN method to produce a high-accuracy community [1,2].…”

Section: Introductionmentioning

confidence: 99%

GN-PPN: Parallel Girvan-Newman-Based Algorithm to Detect Communities in Graph with Positive and Negative Weights

2022

IJIES

View full text Add to dashboard Cite

show abstract

Optimizing Mobile Radio Access Network Spectrum Refarming Using Community Detection Algorithms

Cited by 1 publication

References 21 publications

GN-PPN: Parallel Girvan-Newman-Based Algorithm to Detect Communities in Graph with Positive and Negative Weights

GN-PPN: Parallel Girvan-Newman-Based Algorithm to Detect Communities in Graph with Positive and Negative Weights

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