摘要：AbstractWhen 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.
