… The time-varying multivariate Gaussian distribution and the undirected graph associated
with it, as introduced in Zhou et al.(2008), provide a useful statistical framework for modeling …

… graph, evolves over time then the data are not longer identically distributed. In this paper we
develop a nonparametric method for estimating time varying … change smoothly over time, we …

Undirected graphs are often used to describe high dimensional distributions. Under sparsity
conditions, the graph can … If the distribution, and hence the graph, evolves over time then the …

… Defining connectedness for pairs of nodes in a directed graph is more complex than in
an undirected graph, because a directed path may exist through the network from vertex i to …

This paper considers a class of distributed quadratic optimization problem under an undirected
and connected graph. Different from most of the existing distributed optimization works …

… A related measure could reflect how balanced the graph is with respect to the time dimension,
since the metrics of interest are time dependent (eg the temporal diameter of the TVG in …

… The main contribution of this paper is to integrate the existing models, concepts, and results
found in the literature into a unified framework that we call TVG (for timevarying graphs). This …

This paper presents a flocking algorithm for networked multi-agent systems with unknown,
nonlinear, time-varying uncertainties by integrating cooperative control and L 1 adaptive …

… We will represent the network connecting the different agents via a time varying graph. This
allows for applications such as sensor and vehicle networks in which some communication …

… for timevarying GTI from online data that can be specialized to a variety of static graph learning
… we recall that the number of total edges in an undirected graph of N nodes is N(N − 1)/2. …