School of Computer Science and Engineering, Beihang University, Beijing, 100191, China.
Department of Computer Science University of Illinois at Chicago, Chicago, 60607, United States.
Sci Rep. 2017 Jul 12;7(1):5221. doi: 10.1038/s41598-017-04725-2.
Inferring the network structure from limited observable data is significant in molecular biology, communication and many other areas. It is challenging, primarily because the observable data are sparse, finite and noisy. The development of machine learning and network structure study provides a great chance to solve the problem. In this paper, we propose an iterative smoothing algorithm with structure sparsity (ISSS) method. The elastic penalty in the model is introduced for the sparse solution, identifying group features and avoiding over-fitting, and the total variation (TV) penalty in the model can effectively utilize the structure information to identify the neighborhood of the vertices. Due to the non-smoothness of the elastic and structural TV penalties, an efficient algorithm with the Nesterov's smoothing optimization technique is proposed to solve the non-smooth problem. The experimental results on both synthetic and real-world networks show that the proposed model is robust against insufficient data and high noise. In addition, we investigate many factors that play important roles in identifying the performance of ISSS.
从有限的可观察数据中推断网络结构在分子生物学、通信和许多其他领域都具有重要意义。这是一项具有挑战性的任务，主要是因为可观察数据是稀疏的、有限的和有噪声的。机器学习和网络结构研究的发展为解决这个问题提供了一个很好的机会。在本文中，我们提出了一种具有结构稀疏性的迭代平滑算法（ISSS）方法。模型中的弹性惩罚用于稀疏解，识别组特征并避免过拟合，模型中的总变差（TV）惩罚可以有效地利用结构信息来识别顶点的邻域。由于弹性和结构 TV 惩罚的非光滑性，提出了一种具有 Nesterov 平滑优化技术的高效算法来解决非光滑问题。在合成和真实网络上的实验结果表明，所提出的模型对数据不足和高噪声具有鲁棒性。此外，我们还研究了许多在识别 ISSS 性能方面起着重要作用的因素。
