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A Social Network Analysis Approach to COVID-19 Community Detection Techniques
Tanupriya Choudhury1, Rohini Arunachalam2, Abhirup Khanna3
1Informatics Cluster, School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun 248007, India.
Summary
Machine learning and social network analysis reveal COVID-19 community clusters. Identifying influential sub-communities can improve public health information dissemination.
Area of Science:
- Network analysis
- Machine learning
- Public health
Background:
- Assessed the geographical distribution of Tambaram, Chennai, and its public health care units.
- Examined the spatial distribution and spatiotemporal clustering of public health care units over four months in the Tambaram zone.
Purpose of the Study:
- To apply machine learning and social network analysis for COVID-19 awareness.
- To identify and analyze community structures within geographical health networks.
Main Methods:
- Employed machine learning for network analysis and community detection.
- Utilized social network analysis to describe clustering and classification processes.
- Conducted an empirical evaluation of two search strategies on a synthetic network.
- Analyzed spatial information and sociogram structures for graph segmentation based on clustering coefficients.
Main Results:
- Identified cohesive blocks with a density value of 5.86 for cluster two, accounting for 74.2% of the network.
- Demonstrated that sub-communities possess greater influence for information sharing.
Conclusions:
- Machine learning and network analysis are effective tools for understanding health-related networks.
- Findings suggest leveraging influential sub-communities for targeted public health messaging and COVID-19 awareness campaigns.
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