Hybrid global structure model for identifying impactful influential nodes in network analysis
Network analysis or graph analytics is crucial in identifying impactful nodes in complex networks, which are prevalent across diverse domains and display intricate structures and interactions. Understanding the significance of nodes within these networks is essential for uncovering their dynamics an...
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| Format: | Thesis |
| Language: | English English |
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2024
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| Online Access: | http://eprints.utem.edu.my/id/eprint/28558/ https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=124267 |
| Abstract | Abstract here |
| Summary: | Network analysis or graph analytics is crucial in identifying impactful nodes in complex networks, which are prevalent across diverse domains and display intricate structures and interactions. Understanding the significance of nodes within these networks is essential for uncovering their dynamics and functionalities. However, conventional centrality measures often struggle to capture the complexities of real-world networks, necessitating innovative solutions. While combining multiple centrality measures shows promise, optimizing these combinations remains challenging. Existing methods, such as the Global Structure Model (GSM), may require revision to fully assess individual nodes' unique influence. To address these gaps, this research introduces a novel hybrid centrality method called Global Structure Model-Degree-Kshell (GDK), integrating both local and global centrality measures. The aim of this research is to provide a more accurate and detailed evaluation of node influence within complex networks. GDK combines various centrality measures to offer comprehensive insights into node importance. Two variants of GDK are presented: GDK-A (addition) and GDK-M (multiplication). The methodology involves a standardized evaluation analysis to compare the performance of GDK-A and GDK-M against conventional centrality methods. Results indicate that GDK-M outperforms both traditional methods and GDK-A, demonstrating superior accuracy and effectiveness. Specifically, GDK-M shows improved performance percentages, highlighting its capability to better identify impactful nodes. This research significantly contributes to both academia and industry by enhancing network analysis techniques, enabling more informed decision-making across various domains. The introduction of the hybrid centrality method opens new possibilities for advancing the understanding of complex network analysis and its real-world applications. By exploring the hidden intricacies of complex networks, this study sheds light on their potential to shape the interconnected world. |
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