Version 1
: Received: 20 February 2024 / Approved: 20 February 2024 / Online: 20 February 2024 (14:55:56 CET)
How to cite:
BENDAHMAN, N.; LOTFI, D. Unveiling Influence in Networks: A Novel Centrality Metric and Comparative Analysis through Graph-Based Models. Preprints2024, 2024021161. https://doi.org/10.20944/preprints202402.1161.v1
BENDAHMAN, N.; LOTFI, D. Unveiling Influence in Networks: A Novel Centrality Metric and Comparative Analysis through Graph-Based Models. Preprints 2024, 2024021161. https://doi.org/10.20944/preprints202402.1161.v1
BENDAHMAN, N.; LOTFI, D. Unveiling Influence in Networks: A Novel Centrality Metric and Comparative Analysis through Graph-Based Models. Preprints2024, 2024021161. https://doi.org/10.20944/preprints202402.1161.v1
APA Style
BENDAHMAN, N., & LOTFI, D. (2024). Unveiling Influence in Networks: A Novel Centrality Metric and Comparative Analysis through Graph-Based Models. Preprints. https://doi.org/10.20944/preprints202402.1161.v1
Chicago/Turabian Style
BENDAHMAN, N. and Dounia LOTFI. 2024 "Unveiling Influence in Networks: A Novel Centrality Metric and Comparative Analysis through Graph-Based Models" Preprints. https://doi.org/10.20944/preprints202402.1161.v1
Abstract
Identifying influential actors within social networks is pivotal for optimizing information flow and mitigating the spread of both rumors and diseases. Several methods have emerged to pinpoint these influential entities in networks that are represented as graphs. In these graphs, nodes corre-spond to individuals and edges indicate their connections. This study focuses on centrality measures, prized for their straightforwardness and effectiveness. We categorize structural central-ity into two: local, considering a node's immediate vicinity, and global, accounting for overarching path structures. Some techniques blend both centralities to highlight nodes influential at both mi-cro and macro levels. Our paper presents a novel centrality measure, accentuating node degree and incorporating the network's broader features, especially paths of different lengths. Through Spearman and Pearson's correlations tested on seven standard datasets, our method proves its merit against traditional centrality measures. Additionally, we employ the SIR model, portraying disease spread, to further validate our approach. The ultimate influential node is gauged by its capacity to infect the most nodes during the SIR model's progression. Our results indicate a nota-ble correlative efficacy across various real-world networks relative to other centrality metrics.
Keywords
Social network; graph; influential actor; centrality measure.
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
