在基于信息和关系强度的复杂网络中识别有影响力的节点
1Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
本研究引入了一种新的图形神经网络 (GNN) 模型,通过考虑关系的强度来准确识别复杂网络中的有影响力的节点. 增强的GNN模型改善了信息聚合,以更好地识别节点影响.
科学领域:
- 网络科学 网络科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 在复杂的网络分析中,识别有影响力的节点至关重要.
- 现有的图形神经网络 (GNN) 往往忽略了节点之间的关系的不同强度.
- 这种限制阻碍了在多种网络中准确评估节点影响.
研究的目的:
- 开发一个改进的GNN模型,以更有效地识别有影响力的节点.
- 解决当前GNN在处理关系优势和网络多样性方面的局限性.
主要方法:
- 使用信息,节点度和平均邻居度构建节点输入特征.
- 开发了一个GNN模型,该模型包含了社区重叠,以确定消息传递的关系强度.
- 在使用SIR模型进行影响评估的12个现实世界网络上验证了模型.
主要成果:
- 拟议的GNN模型通过考虑关系的优势,有效地汇总节点和邻里信息.
- 实验结果表明,与基准方法相比,在识别有影响力的节点方面表现优越.
- 该模型显示了跨多种复杂网络结构的适应性.
结论:
- 新的GNN方法显著提高了复杂网络中影响性节点识别的准确性.
- 考虑关系的优势和采用各种节点特征是提高GNN性能的关键.
- 这种方法为分析网络系统中的影响力学提供了更强大的工具.
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