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Enhancing Real-World Network Understanding through Centrality Measures and Improved Clustering Coefficient Methods
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 60 |
| Year of Publication: 2025 |
| Authors: Touseef Ali, Ubaida Fatima |
10.5120/ijca2025925911
|
Touseef Ali, Ubaida Fatima . Enhancing Real-World Network Understanding through Centrality Measures and Improved Clustering Coefficient Methods. International Journal of Computer Applications. 187, 60 ( Nov 2025), 25-30. DOI=10.5120/ijca2025925911
Abstract
Network analysis has become an essential tool for understanding the complex structures and dynamics of large datasets across various disciplines. The quick growth of data in size and complexity presents significant challenges in accuracy and explanation of existing methods. This research proposes the development and application of advanced community detection algorithms related to large scale networks. Particular emphasis of this will be placed on addressing challenges such as overlapping communities, dynamic network structures, and the balance between computational cost and detection quality. This study seeks to advance the understanding of community detection in large networks and its implications for real world data driven problems.
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