Analysis of the Modern Algorithms’ Accuracy for Communities Identification on Networks when Working with Graph Databases

Ekaterina D. Kazakova; Казакова Екатерина Даниловна

doi:10.33693/2313-223X-2023-10-1-49-59

Analysis of the Modern Algorithms’ Accuracy for Communities Identification on Networks when Working with Graph Databases

作者: Kazakova E.D.¹
隶属关系:
1. Financial University under the Government of the Russian Federation
期: 卷 10, 编号 1 (2023)
页面: 49-59
栏目: MATHEMATICAL MODELING, NUMERICAL METHODS AND COMPLEX PROGRAMS
URL: https://journals.rcsi.science/2313-223X/article/view/250552
DOI: https://doi.org/10.33693/2313-223X-2023-10-1-49-59
ID: 250552

如何引用文章

全文:

开放存取

##reader.subscriptionAccessGranted##
受限制的访问

订阅存取

详细
全文:
作者简介
参考
补充文件
统计

详细

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.

关键词

network analysis, community detection algorithms, graph databases

全文:

##article.viewOnOriginalSite##

作者简介

Ekaterina Kazakova

Financial University under the Government of the Russian Federation

编辑信件的主要联系方式.
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

俄罗斯联邦, Moscow

参考

Barabasi A.-L., Albert R. Emergence of scaling in Random networks. Science. 1999. No. 286. Pp. 509–512. doi: 10.1126/science.286.5439.509.
Blondel V.D., Guillaume J.-L., Lambiotte R., Lefebvre E. Fast unfolding of communities in large networks. Journal of Statistical Mechanics Theory and Experiment. 2008. doi: 10.1088/1742-5468/2008/10/P10008.
Bruna J., Li Xiang. Community detection with graph neural networks. 2017.
Circulo library. URL: http://lab41.github.io/Circulo/
Clauset A., Newman M.E.J., Moore C. Finding community structure in very large networks. Physical Review E. 2004. URL: http:// arxiv.org/abs/cond-mat/0408187
Coscia M., Rossetti G., Giannotti F., Pedreschi D. Demon: A local-first discovery method for overlapping communities. KDD. 2012. URL: http://www. michelecoscia.com/wp-content/uploads/2012/08/cosciakdd12.pdf
Blondel V.D., Guillaume J.-L., Lambiotte R., Lefebvre E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008. URL: http://arxiv.org/abs/0803.0476
Fortunato S. Community detection in graphs. Physics Reports. 2009. URL: http://arxiv.org/abs/0906.0612
Girvan M., Newman M.E.J. Community structure in social and biological networks. Proceedings of the National Academy of Sciences. 2001. URL: http://arxiv.org/abs/cond-mat/0112110
Gregory S. An algorithm to find overlapping community structure in networks. Proceeding PKDD 2007 Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases. 2007. URL: http://www.cs.bris.ac.uk/Publications/Papers/2000689.pdf
Girvan M., Newman M.G.M., Newman M.E.J. Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America. 2002. No. 99. Pp. 7821–7826. doi: 10.1073/pnas.122653799.
Hamilton W.L., Ying R., Leskovec J. Representation learning on graphs: Methods and applications. 2017.
Nikhil M., Carin L., Rai P. Stochastic block models meet graph neural networks. 2019.
Pons P., Latapy M. Computing communities in large networks using random walks. J. Graph Algorithms Appl. 2006. No. 10. Pp. 191–218. doi: 10.7155/jgaa.00124.