True Nonlinear Dynamics from Incomplete Networks
True Nonlinear Dynamics from Incomplete Networks
复制标题
来自不完整网络的真正非线性动力学
DOI：
10.1609/aaai.v34i01.5343
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
M. Magdon
中科院分区：
文献类型：
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作者：
Chunheng Jiang;Jianxi Gao;M. Magdon
我们研究复杂网络上的非线性动力学。每个顶点i都有一个状态xi，根据网络动力学演化为一个稳态xi*。我们开发了基本的工具来学习网络的一小部分的真实稳态，而不知道整个网络。一种朴素的方法和目前最先进的技术是遵循观察到的局部网络的动力学达到局部平衡。这显然无法提取出真正的稳态。我们使用平均场方法将网络中不可见部分的动态映射到单个节点，这使我们能够在从生态到社会网络到基因调控等领域的5个观察到的顶点上恢复对稳态的准确估计。不完全网络在实践中是常态，我们提供了新的方法来思考只有稀疏信息时的非线性动力学。
We study nonlinear dynamics on complex networks. Each vertex i has a state xi which evolves according to a networked dynamics to a steady-state xi*. We develop fundamental tools to learn the true steady-state of a small part of the network, without knowing the full network. A naive approach and the current state-of-the-art is to follow the dynamics of the observed partial network to local equilibrium. This dramatically fails to extract the true steady state. We use a mean-field approach to map the dynamics of the unseen part of the network to a single node, which allows us to recover accurate estimates of steady-state on as few as 5 observed vertices in domains ranging from ecology to social networks to gene regulation. Incomplete networks are the norm in practice, and we offer new ways to think about nonlinear dynamics when only sparse information is available.