整合机器学习技术,用于在复杂网络中识别关键节点
Madupuri ReddyPriya1, Murali Krishna Enduri2, Koduru Hajarathaiah3
1Department of Computer Science and Engineering, SRM University-AP, Andhra Pradesh, India.
Scientific reports
|February 26, 2026
概括
本研究引入了一种机器学习方法,用于识别复杂网络中的有影响力的节点,其性能优于传统方法. 该框架将网络结构与感染动态集成在一起,以便在传播场景中更好地预测.
科学领域:
- 网络科学 网络科学
- 计算社会科学 计算社会科学
- 机器学习 机器学习
背景情况:
- 识别有影响力的节点对于在流行病控制等领域的网络分析至关重要.
- 传统的中心性测量通常无法在动态场景中捕捉复杂的节点行为.
- 现有的方法忽视了拓特征和传播能力之间的非线性依赖.
研究的目的:
- 开发基于机器学习的框架,以准确识别有影响力的节点.
- 在动态网络传输中克服传统中心性措施的局限性.
- 整合网络拓与疾病传播动态,以提高节点突出性预测.
主要方法:
- 构建的节点特征向量集成感染率和拓特征.
- 使用SIR (易受感染-感染-恢复) 和IC (独立级联) 模型进行传播模拟.
- 评估的独立分类器 (SVM,KNN,随机森林) 和混合SVM+K-means方法.
主要成果:
- 拟议的机器学习框架显著优于传统的中心性措施.
- 混合SVM+K-means方法有效地捕捉了节点特征和传播能力之间的复杂关系.
- 与传统方法相比,识别有影响力的节点的准确性提高了15%至45%.
结论:
- 机器学习与网络属性相结合,为识别基本节点提供了有效和可扩展的策略.
- 拟议的方法提高了在复杂网络中影响性节点检测的准确性.
- 整合诸如感染率之类的动态属性,可以提高节点传播能力的预测.
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