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InfoTech Spectrum: Iraqi Journal of Data Science

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Research article Open Access EN pp. 1–15 · 30 Jan 2025

Using Density Criterion and Increasing Modularity to Detect Communities in Complex Networks

  1. Department of Computer Engineering, Islamic Azad University, Isfahan , Iran
  2. Department of Computer Engineering, Ilam Branch, Islamic Azad University, Ilam, Iran,‎
✱ Corresponding author: [email protected]
DOI 10.51173/ijds.v2i1.63 IJDS-TRK-2026-63 3 views 0 downloads

Abstract

The selection of the initial centers of the communities is also significant in iteration-based methods for finding the communities in the networks. This is the reason why, if the initial centers of the communities are not chosen correctly, the errors and the time required for the application of the algorithm in the detection of the communities will be higher. Hence, selecting more significant nodes as starting points of communities can be the appropriate solution. Various techniques can be employed to achieve the selection of more significant nodes. In this thesis, the algorithm under discussion employs density and modularity criteria in the identification of communities in complex networks. This algorithm initially defines the number of nodes or the distinctive members of the community, in which these nodes have higher density levels and all the other nodes in their neighborhood have lower density levels. Next, the local communities are defined as the nodes that are in some way connected to the core nodes. Finally, the final communities are defined with the assistance of the merging algorithm, which is based on increasing modularity. In this algorithm, increasing modularity is used as a criterion for joining local communities together. Modularity is a criterion that indicates how the graph is like a modular or an organized community. When modularity becomes higher, local communities merge to form the final community. This means that it is possible to apply the presented algorithm and to use both density and modularity criteria to detect communities in complex networks. When the core nodes and local communities are first detected and then merged based on the increasing value of modularity, the resultant communities are more accurate. The results of the conducted experiments prove that the method applied in the Karate Club network clustering is equal to 0. 6913 for the NMI criterion and a value of 0. 733 for the accuracy criterion.
Keywords
Modularity (biology) Computer science Data science Biology Evolutionary biology

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Iman Hasan Abed and Sondos Bahadori. 2025. Using Density Criterion and Increasing Modularity to Detect Communities in Complex Networks. InfoTech Spectrum: Iraqi Journal of Data Science 2, 1 (2025), 1–15. https://doi.org/10.51173/ijds.v2i1.63

Abed, I. H. ; Bahadori, S.. Using Density Criterion and Increasing Modularity to Detect Communities in Complex Networks. InfoTech Spectrum: Iraqi Journal of Data Science 2025, 2 (1), 1–15. https://doi.org/10.51173/ijds.v2i1.63

Abed, I. H. & Bahadori, S. (2025). Using Density Criterion and Increasing Modularity to Detect Communities in Complex Networks. InfoTech Spectrum: Iraqi Journal of Data Science, 2(1), 1–15. https://doi.org/10.51173/ijds.v2i1.63

ABED, Iman Hasan ; BAHADORI, Sondos. Using Density Criterion and Increasing Modularity to Detect Communities in Complex Networks. InfoTech Spectrum: Iraqi Journal of Data Science, v. 2, n. 1, p. 1–15, 2025. https://doi.org/10.51173/ijds.v2i1.63

Abed, Iman Hasan and Bahadori, Sondos. 2025. "Using Density Criterion and Increasing Modularity to Detect Communities in Complex Networks." InfoTech Spectrum: Iraqi Journal of Data Science 2 (1): 1–15. https://doi.org/10.51173/ijds.v2i1.63

Abed, I. H. and Bahadori, S. (2025) 'Using Density Criterion and Increasing Modularity to Detect Communities in Complex Networks', InfoTech Spectrum: Iraqi Journal of Data Science, 2(1), pp. 1–15. https://doi.org/10.51173/ijds.v2i1.63

I. H. Abed and S. Bahadori, "Using Density Criterion and Increasing Modularity to Detect Communities in Complex Networks," InfoTech Spectrum: Iraqi Journal of Data Science, vol. 2, no. 1, pp. 1–15, 2025. doi: https://doi.org/10.51173/ijds.v2i1.63.

Abed, Iman Hasan and Bahadori, Sondos. "Using Density Criterion and Increasing Modularity to Detect Communities in Complex Networks." InfoTech Spectrum: Iraqi Journal of Data Science, vol. 2, no. 1, 2025, pp. 1–15. https://doi.org/10.51173/ijds.v2i1.63

Iman Hasan Abed and Sondos Bahadori. "Using Density Criterion and Increasing Modularity to Detect Communities in Complex Networks." InfoTech Spectrum: Iraqi Journal of Data Science 2, no. 1 (2025): 1–15. https://doi.org/10.51173/ijds.v2i1.63

Abed IH , Bahadori S. Using Density Criterion and Increasing Modularity to Detect Communities in Complex Networks. InfoTech Spectrum: Iraqi Journal of Data Science. 2025;2(1):1–15. https://doi.org/10.51173/ijds.v2i1.63

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