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 |
Dividing Social Networks into Two Communities Using the Maximum Likelihood Method: Application to ESG
1 Andijan State University, Andijan, Uzbekistan
2 Tashkent State Economic University, Tashkent, Uzbekistan
3 Soil and Water Department, Faculty of Agriculture, Sohag University, Sohag, Egypt
* Corresponding author: birinchi_dilshod@mail.ru
This article explores the application of the Maximum Likelihood Estimation method (MLE) for community detection in environmental, social, and governance (ESG) networks. ESG factors are important in assessing the sustainability and ethical impact of investments. By understanding the structure of social networks that discuss and promote ESG practices, we can gain important insights. It proposes a probabilistic framework for identifying community structures by dividing the network into two distinct groups based on connectivity patterns using the MLE method. The network structure is analyzed, and the method identifies groups of united organizations such as companies, investors, and NGOs with similar ESG orientations and interaction patterns. The results reveal important insights into how ESG information flows within and between these communities, highlighting key influencers and central nodes whose connections play a key role in the diffusion of ESG practices. These conclusions can be important in developing targeted communication strategies, identifying potential opportunities for cooperation, and forming informed investment decisions. By providing a solid framework for analyzing ESG networks, this paper is relevant to a broader understanding of ESG dynamics and supports the development of a more sustainable and interconnected global ecosystem.
Key words: Maximum likelihood method / Community detection / ESG networks / Social networks
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.