用特征选择提升复杂网络仿真精度与效率
How the use of feature selection methods influences the efficiency and accuracy of complex network simulations
- 提出无监督筛选+包装器组合的特征选择方法FS-SNS
- 4个特征为最优，8/10真实网络仿真准确率提升
- 适合数字孪生与复杂网络系统研究者参考
复杂网络模型旨在通过仿真与链接预测精准模拟现实世界网络。网络由具有真实属性的节点及其连接构成，呈现异质性特征。当前多数网络模型未充分融入真实特征，本研究提出基于无监督过滤方法对节点特征排序，并结合包装器函数测试特征组合，构建新方法FS-SNS。实验显示该方法在10个真实网络中提升了8个的仿真效果，且发现4个特征为达到最高精度的稳定阈值。研究还探讨了其在数字孪生与复杂网络系统领域的应用前景。
原文摘要 · Abstract (English)
Complex network systems' models are designed to perfectly emulate real-world networks through the use of simulation and link prediction. Complex network systems are defined by nodes and their connections where both have real-world features that result in a heterogeneous network in which each of the nodes has distinct characteristics. Thus, incorporating real-world features is an important component to achieve a simulation which best represents the real-world. Currently very few complex network systems implement real-world features, thus this study proposes feature selection methods which utilise unsupervised filtering techniques to rank real-world node features alongside a wrapper function to test combinations of the ranked features. The chosen method was coined FS-SNS which improved 8 out of 10 simulations of real-world networks. A consistent threshold of included features was also discovered which saw a threshold of 4 features to achieve the most accurate simulation for all networks. Through these findings the study also proposes future work and discusses how the findings can be used to further the Digital Twin and complex network system field.
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