Analysis of the Modern Algorithms’ Accuracy for Communities Identification on Networks when Working with Graph Databases
- Authors: Kazakova E.D.1
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Affiliations:
- Financial University under the Government of the Russian Federation
- Issue: Vol 10, No 1 (2023)
- Pages: 49-59
- Section: MATHEMATICAL MODELING, NUMERICAL METHODS AND COMPLEX PROGRAMS
- URL: https://journals.eco-vector.com/2313-223X/article/view/545838
- DOI: https://doi.org/10.33693/2313-223X-2023-10-1-49-59
- ID: 545838
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Abstract
In this paper, we consider methods for extracting communities in networksusing various algorithms. The Girvan-Newman, Louvain, Walktrap and Leiden algorithms were presented and the results of their application on the Wikipedia graph were analyzed. Various metrics were used to assess the quality of the isolated communities, and the results were stored in the Neo4j graph database. The results showed that the Leiden and Louvain algorithms with a resolution equal to one showed the best results compared to other algorithms.
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About the authors
Ekaterina D. Kazakova
Financial University under the Government of the Russian Federation
Author for correspondence.
Email: 191841@edu.fa.ru
student at the Faculty of Information Technology and Big Data Analysis of the Financial University under the Government of the Russian Federation
Russian Federation, MoscowReferences
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Supplementary files
Supplementary Files
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Fig. 1. Example of the simple graph
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Fig. 2. Graph retained from the networkx
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Fig. 3. Complete database
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Fig. 4. Node centrality graph
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Fig. 5. Identification of communities by the Girvan-Newman algorithm
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Fig. 6. Identification of communities by the Louvain algorithm
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Fig. 7. Identification of communities by the Leiden algorithm
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Fig. 8. Identification of communities by the Walktrap algorithm
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Algorithm quality metrics
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Fig. 9. Friedman rank test
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Fig. 10. Similarity matrix for random walk and Leiden algorithms
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