Open Access
| Issue |
E3S Web Conf.
Volume 574, 2024
1st International Scientific Conference “Green Taxonomy for Sustainable Development: From Green Technologies to Green Economy” (CONGREENTAX-2024)
|
|
|---|---|---|
| Article Number | 03007 | |
| Number of page(s) | 14 | |
| Section | Environmental, Social, and Governance (ESG) | |
| DOI | https://doi.org/10.1051/e3sconf/202457403007 | |
| Published online | 02 October 2024 | |
- M.E.J. Newman, M. Girvan, Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004) [CrossRef] [PubMed] [Google Scholar]
- S. Fortunato, Community detection in graphs. Phys. Rep. 486 (2010) 75-174 [CrossRef] [Google Scholar]
- K. Nowicki, T.A.B. Snijders, Estimation and prediction for stochastic blockstructures. J. Am. Stat. Assoc. 96 (2001) 1077-1087 [CrossRef] [Google Scholar]
- T.P. Peixoto, Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Phys. Rev. E 89, 012804 (2014) [CrossRef] [PubMed] [Google Scholar]
- J. Yang, J. Leskovec, Community-affiliation graph model for overlapping network community detection. Proc. of the 2012 IEEE 12th Int. Conf. on Data Mining (2012) 1170-1175 [Google Scholar]
- B. Karrer, M.E.J. Newman, Stochastic blockmodels and community structure in networks. Phys. Rev. E 83, 016107 (2011) [CrossRef] [PubMed] [Google Scholar]
- L. Peel, D.B. Larremore, A. Clauset, The ground truth about metadata and community detection in networks. Sci. Adv. 3, e1602548 (2017) [CrossRef] [Google Scholar]
- A. Lancichinetti, S. Fortunato, Community detection algorithms: A comparative analysis. Phys. Rev. E 80, 056117 (2009) [CrossRef] [PubMed] [Google Scholar]
- M.E.J. Newman, Equivalence between modularity optimization and maximum likelihood methods for community detection. Phys. Rev. E 94, 052315 (2016) [CrossRef] [PubMed] [Google Scholar]
- A. Abdushukurov, D. Zakhidov, Social networks: Effective methods of dividing into two and three groups. AIP Conference Proceedings 3147 (2024) [Google Scholar]
- A. Clauset, M.E.J. Newman, C. Moore, Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004) [CrossRef] [PubMed] [Google Scholar]
- A. Lancichinetti, S. Fortunato, F. Radicchi, Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 046110 (2008) [CrossRef] [PubMed] [Google Scholar]
- V.D. Blondel, J.L. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. 2008, P10008 (2008) [CrossRef] [Google Scholar]
- M. Rosvall, C.T. Bergstrom, Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences 105 (2008) 1118-1123 [CrossRef] [PubMed] [Google Scholar]
- M. Menner, M. N. Zeilinger,, Maximum likelihood methods for inverse learning of optimal controllers. IFAC-PapersOnLine 53 (2020) 5266-5272 [CrossRef] [Google Scholar]
- G. Palla, I. Derényi, I. Farkas, T. Vicsek, Uncovering the overlapping community structure of complex networks in nature and society. Nature 435 (2005) 814-818 [CrossRef] [PubMed] [Google Scholar]
- J. Li, Analysis of snow load probabilistic models and calculation of reference snow pressure using maximum likelihood method for multiple cities in Liaoning Province, China. Structures 57, 105257 (2023) [CrossRef] [Google Scholar]
- T. Zhang, Probabilistic model of the pre-corrosion fatigue life of epoxy-coated aluminum alloys based on the single point-group model of the maximum likelihood method. Int. J. Fatigue 171, 107580 (2023) [CrossRef] [Google Scholar]
- M.S. Chowdhury, Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting. Environmental Challenges 14, 100800 (2024) [CrossRef] [Google Scholar]
- M. Li, X. Liu, Maximum likelihood least squares based iterative estimation for a class of bilinear systems using the data filtering technique. International Journal of Control, Automation and Systems 18 (2020) 1581-1592 [CrossRef] [Google Scholar]
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