2021/10/19 by Rohan Money, Joshin Krishnan, Money, Rohan +4
Computer Science · Engineering · Physics and Astronomy · Social Sciences · #Advanced Computing and Algorithms #Advanced Graph Neural Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Signal Processing (eess.SP) #cs.LG #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2110.09935
arxiv created 2021/10/19 · openalex publication_date 2021/10/19 · arxiv updated 2021/10/20 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Online topology estimation of graph-connected time series is challenging, especially since the causal dependencies in many real-world networks are nonlinear. In this paper, we propose a kernel-based algorithm for graph topology estimation. The algorithm uses a Fourier-based Random feature approximation to tackle the curse of dimensionality associated with the kernel representations. Exploiting the fact that the real-world networks often exhibit sparse topologies, we propose a group lasso based optimization framework, which is solve using an iterative composite objective mirror descent method, yielding an online algorithm with fixed computational complexity per iteration. The experiments conducted on real and synthetic data show that the proposed method outperforms its competitors.