  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.
