Joint Network Topology and Dynamics Recovery From Perturbed Stationary Points
Joint Network Topology and Dynamics Recovery From Perturbed Stationary Points
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DOI：
10.1109/tsp.2019.2929471
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发表时间：
2019-07
影响因子：
5.4
通讯作者：
Hoi-To Wai;A. Scaglione;B. Barzel;Amir Leshem
中科院分区：
文献类型：
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作者：
Hoi-To Wai;A. Scaglione;B. Barzel;Amir Leshem
本文提出了一种推理方法，可以根据观察到扰动的固定点的观察来学习复杂系统的模型。我们建议通过正则回归公式共同估计动力学参数和网络拓扑。我们方法的杰出特征取决于等级缺陷网络数据的直接建模，该数据在网络科学中广泛发现，但在先前的研究中经常被忽略。新的建模技术使我们能够在数据不足的情况下提供网络可识别性条件。在已知动力学参数的特殊情况下，我们展示了图结构中的动力学和稀疏性之间的相互作用如何导致网络可识别性的可验证条件。此外，仅依靠稳态方程，我们的方法避免了记录瞬态数据的必要性，并允许有意义地组合来自多个实验的数据。在具有基因调节网络和意见动态的示例上进行数值实验，以证明我们的主张是合理的。
This paper presents an inference method to learn a model for complex system based on observations of the perturbed stationary points. We propose to jointly estimate the dynamics parameters and network topology through a regularized regression formulation. A distinguished feature of our approach rests on the direct modeling of rank deficient network data, which is widely found in network science but frequently ignored in the prior research. The new modeling technique allows us to provide the network identifiability condition under the scenarios of insufficient data. In the special case where the dynamics parameters are known, we show how the interplay between dynamics and sparsity in the graph structure can lead to verifiable conditions for network identifiability. Furthermore, relying only on the steady-states equations, our method avoids the necessity to record transient data, and allows to meaningfully combine data from multiple experiments. Numerical experiments are performed on examples with gene regulatory networks and opinion dynamics to justify our claims.