揭示网络中的影响力:通过基于图形的模型进行新的中心性度量和比较分析
Nada Bendahman1, Dounia Lotfi1
1LRIT, Faculty of Sciences, Mohammed V University in Rabat, Rabat 10000, Morocco.
Entropy (Basel, Switzerland)
|June 26, 2024
概括
本研究引入了一项新的网络中心性衡量标准,以确定关键影响者. 它有效地识别了在社交网络中信息流动和疾病传播缓解的有影响力的节点.
科学领域:
- 网络科学 网络科学
- 计算社会科学 计算社会科学
- 图形理论 图形理论
背景情况:
- 识别社交网络中的有影响力的节点对于管理信息传播和疾病传播至关重要.
- 集中度指标是网络分析中量化节点影响的关键工具.
- 现有的方法往往侧重于本地或全球网络结构,有些方法结合了两者.
研究的目的:
- 提出一种新的中心性测量方法,将节点程度与更广泛的网络路径特征集成在一起.
- 根据传统的中心性指标来评估拟议措施的有效性.
- 为了验证测量对预测现实世界的网络动态,如疾病传播的实用性.
主要方法:
- 开发了一个新的中心性测量方法,强调节点程度,并结合网络路径信息.
- 使用七个标准网络数据集进行了相关性分析 (斯皮尔曼,皮尔森).
- 利用易受感染-恢复 (SIR) 模型来模拟病毒传播和评估节点影响能力.
主要成果:
- 新型中心性测量结果显示,与各种数据集的既定指标相比,新型中心性测量结果具有显著的相关有效性.
- 使用SIR模型的验证证实了该措施能够识别具有高感染潜力的节点.
- 拟议的方法在识别各种网络结构中的有影响力的参与者方面表现强.
结论:
- 新的中心性测量提供了一种有效的方法来识别社交网络中的有影响力的节点.
- 这种方法提高了对网络结构及其对信息和疾病传播的影响的理解.
- 这些发现对基于网络的场景中的有针对性的干预措施有影响.
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