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 07005
Number of page(s) 12
Section Organizational Aspects of Sustainable Green Development
DOI https://doi.org/10.1051/e3sconf/202457407005
Published online 02 October 2024
  1. K.L. Akerlof, The growth and disciplinary convergence of environmental communication: A bibliometric analysis of the field (1970–2019). Front. Environ. Sci. 9, 814599 (2022) [CrossRef] [Google Scholar]
  2. R.E. Fuoco, Effective communications strategies to increase the impact of environmental health research. Environmental Health 22, 1 (2023) [CrossRef] [Google Scholar]
  3. D. Byrnes, L. Blum, W. Walker, Undisciplining environmental communication pedagogy: Toward environmental and epistemic justice in the interdisciplinary sustainability classroom. Sustainability 15, 1 (2022) [Google Scholar]
  4. A.S. Iquebal, S. Bukkapatna, Consistent estimation of the max-flow problem: Towards unsupervised image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 44 (2020) 2346-2357 [Google Scholar]
  5. N. Greggio, Fast estimation of Gaussian mixture models for image segmentation. Mach. Vis. Appl. 23 (2012) 773-789 [CrossRef] [Google Scholar]
  6. S. Chowdhury, Cell segmentation by multi-resolution analysis and maximum likelihood estimation (MAMLE). BMC Bioinformatics 14 (2013) 1-13 [Google Scholar]
  7. A. Abdushukurov, D. Zakhidov, Social networks: Effective methods of dividing into two and three groups. AIP Conf. Proc. 3147 (2024) [Google Scholar]
  8. A. Lancichinetti, S. Fortunato, Community detection algorithms: A comparative analysis. Phys. Rev. E 80, 056117 (2009) [CrossRef] [PubMed] [Google Scholar]
  9. 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]
  10. 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]
  11. A. Lancichinetti, S. Fortunato, F. Radicchi, Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78, 046110 (2008) [CrossRef] [PubMed] [Google Scholar]
  12. 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]
  13. 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]
  14. M. Girvan, M. E.J. Newman, Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99 (2002) 7821-7826 [CrossRef] [PubMed] [Google Scholar]
  15. G. Palla, I. Derényi, I. Farkas, T. Vicsek, Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 7043 (2005) 814-818 [CrossRef] [PubMed] [Google Scholar]
  16. R. Aldecoa, I. Marín, Exploring the limits of community detection strategies in complex networks. Sci. Rep. 3, 2216 (2013) [CrossRef] [Google Scholar]
  17. A. Lancichinetti, S. Fortunato, J. Kertész, Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys. 11, 033015 (2009) [CrossRef] [Google Scholar]
  18. S. Fortunato, M. Barthelemy, Resolution limit in community detection. Proceedings of the National Academy of Sciences 104 (2007) 36-41 [CrossRef] [PubMed] [Google Scholar]
  19. W.W. Zachary, An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33 (1977) 452-473 [CrossRef] [Google Scholar]
  20. B. Karrer, M.E.J. Newman, Stochastic blockmodels and community structure in networks. Phys. Rev. E 83, 016107 (2011) [CrossRef] [PubMed] [Google Scholar]

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