We reconsider randomized algorithms for the low-rank approximation of SPSD matrices
such as Laplacian and kernel matrices that arise in data analysis and machine learning …

Eigenvector centrality is a common measure of the importance of nodes in a network. Here
we show that under common conditions the eigenvector centrality displays a localization …

Numerous centrality measures have been developed to quantify the importances of nodes in
time-independent networks, and many of them can be expressed as the leading eigenvector …

This paper builds theoretical foundations for the recovery of a newly proposed class of
smooth graph signals, approximately bandlimited graph signals, under three sampling …

We study diffusion-driven pattern formation in networks of networks, a class of multilayer
systems, where different layers have the same topology, but different internal dynamics …

This paper is motivated by the task of detecting anomalies in networks of financial
transactions, with accounts as nodes and a directed weighted edge between two nodes …

The spectrum of the adjacency matrix plays several important roles in the mathematical
theory of networks and network data analysis, for example in percolation theory, community …

Finding group elements from noisy measurements of their pairwise ratios is also known as
the group synchronization problem, first introduced in the context of the group SO (2) of …

We present a framework for representing and modeling data on graphs. Based on this
framework, we study three typical classes of graph signals: smooth graph signals, piecewise …

Complex networks with directed, local interactions are ubiquitous in nature and often occur
with probabilistic connections due to both intrinsic stochasticity and disordered …