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

For a given graph G, each partition of the vertices has a modularity score, with higher values
indicating that the partition better captures community structure in G. The modularity q∗(G) of …

We consider the nonlinear graph p-Laplacian and the set of eigenvalues and associated
eigenfunctions of this operator defined by a variational principle. We prove a nodal domain …

Inspired by the linear Schrödinger operator, we consider a generalized p-Laplacian operator
on discrete graphs and present new results that characterize several spectral properties of …

A wide variety of application domains is concerned with data consisting of entities and their
relationships or connections, formally represented as graphs. Within these diverse …

The self-consistent field (SCF) iteration, combined with its variants, is one of the most widely
used algorithms in quantum chemistry. We propose a procedure to adapt the SCF iteration …

Random graph models are a recurring tool‐of‐the‐trade for studying network structural
properties and benchmarking community detection and other network algorithms. Moreover …

Maximizing the modularity of a network is a successful tool to identify an important
community of nodes. However, this combinatorial optimization problem is known to be NP …

Revealing a community structure in a network or dataset is a central problem arising in many
scientific areas. The modularity function Q is an established measure quantifying the quality …

In a graph or complex network, communities and anti-communities are node sets whose
modularity attains extremely large values, positive and negative, respectively. We consider …