Network scientists have shown that there is great value in studying pairwise interactions
between components in a system. From a linear algebra point of view, this involves defining …

Eigenvector centrality is a standard network analysis tool for determining the importance of
(or ranking of) entities in a connected system that is represented by a graph. However, many …

Eigenvector-based centrality measures are among the most popular centrality measures in
network science. The underlying idea is intuitive and the mathematical description is …

Core–periphery detection aims to separate the nodes of a complex network into two subsets:
a core that is densely connected to the entire network and a periphery that is densely …

Label spreading is a general technique for semi-supervised learning with point cloud or
network data, which can be interpreted as a diffusion of labels on a graph. While there are …

We derive and analyze a new iterative algorithm for detecting network core--periphery
structure. Using techniques from nonlinear Perron--Frobenius theory, we prove global …

We present a unifying Perron--Frobenius theory for nonlinear spectral problems defined in
terms of nonnegative tensors. By using the concept of tensor shape partition, our results …

In recent years, many centralities have been developed to identify the key nodes in
multilayer and temporal networks. Among these centrality measures, eigenvector-based …

We introduce the concept of shape partition of a tensor and formulate a general tensor
eigenvalue problem that includes all previously studied eigenvalue problems as special …

Multilayer networks provide a powerful framework for modeling complex systems that
capture different types of interactions between the same set of entities across multiple layers …