Mapping flows on sparse networks with missing links.,Physical Review E

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Mapping flows on sparse networks with missing links.
Physical Review E ( IF 2.2 ) Pub Date : 2020-07-06 , DOI: 10.1103/physreve.102.012302
Jelena Smiljanić _{1,

2} , Daniel Edler _{1,

3,

4} , Martin Rosvall ₁

Affiliation

Unreliable network data can cause community-detection methods to overfit and highlight spurious structures with misleading information about the organization and function of complex systems. Here we show how to detect significant flow-based communities in sparse networks with missing links using the map equation. Since the map equation builds on Shannon entropy estimation, it assumes complete data such that analyzing undersampled networks can lead to overfitting. To overcome this problem, we incorporate a Bayesian approach with assumptions about network uncertainties into the map equation framework. Results in both synthetic and real-world networks show that the Bayesian estimate of the map equation provides a principled approach to revealing significant structures in undersampled networks.

中文翻译：

映射在缺少链接的稀疏网络上流动。

不可靠的网络数据可能会导致社区检测方法过度适合并突出虚假结构，并带有关于复杂系统的组织和功能的误导性信息。在这里，我们展示了如何使用地图方程在缺少链接的稀疏网络中检测基于流量的重要社区。由于映射方程式基于Shannon熵估计，因此它假设使用了完整的数据，因此对欠采样网络的分析可能导致过度拟合。为了克服这个问题，我们将带有网络不确定性假设的贝叶斯方法纳入地图方程框架。综合网络和实际网络中的结果均表明，地图方程的贝叶斯估计为揭示欠采样网络中的重要结构提供了一种有原则的方法。

更新日期：2020-07-06

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