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Reconstruction of time-varying small-world networks incorporating structural priors

机译：结合结构先验的时变小世界网络的重构

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When inferring the undergoing network structure, which describes the dynamic mutual influence among large scale variables, it is a challenge to take full advantage of structural prior information when it is available. In this paper, we focus on reconstruction of piecewise-constant time-varying small-world networks. Specifically, we propose an identification method incorporating structural properties as prior information, including the average degree of the network. On the one hand, we adjust the network sparsity by re-weightingl1norm according to the deviation of the estimated average degree, based on the assumption that the average degree is almost constant over time. On the other hand, for each node in the network, we encourage the existence of potential associated edges while discouraging non-existing edges based on predictions from the previous iteration. Finally, an adaptive LASSO algorithm is utilized to uncover the time-varying structures which performs better on small-world networks when comparing with the method without prior information.
机译：当推断正在经历的网络结构（描述大型变量之间的动态相互影响）时，充分利用结构先验信息（如果可用）是一个挑战。在本文中，我们专注于分段恒定时变小世界网络的重建。具体来说，我们提出一种结合结构特性作为先验信息（包括网络的平均程度）的识别方法。一方面，我们基于估计的平均程度随时间变化的假设，根据估计的平均程度的偏差，通过重新加权11范数来调整网络稀疏性。另一方面，对于网络中的每个节点，我们都鼓励存在潜在的关联边缘，同时根据先前迭代的预测，在不鼓励不存在的边缘的情况下鼓励这样做。最后，与没有先验信息的方法相比，自适应的LASSO算法被用来发现时变结构，该结构在小世界网络上表现更好。

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