We survey the concept of assortativity, starting from its original definition by Newman in
2002. Degree assortativity is the most commonly used form of assortativity. Degree …

This paper provides a compressive literature review on the application of complex network
theories in the resilience evaluation and enhancement of modern power systems. First, the …

Abstract Message Passing Neural Networks (MPNNs) are instances of Graph Neural
Networks that leverage the graph to send messages over the edges. This inductive bias …

In this paper, we propose a family of nested weighted n-polygon networks, which is a kind of
promotion of infinite fractal dimension networks. We study the coherence of the networks …

Comparison of graph structure is a ubiquitous task in data analysis and machine learning,
with diverse applications in fields such as neuroscience, cyber security, social network …

The study of network robustness is a critical tool in the characterization and sense making of
complex interconnected systems such as infrastructure, communication and social networks …

The relationship between anatomic connectivity of large-scale brain networks and their
functional connectivity is of immense importance and an area of active research. Previous …

The general method of graph coarsening or graph reduction has been a remarkably useful
and ubiquitous tool in scientific computing and it is now just starting to have a similar impact …

Network robustness refers to as the capacity to absorb disturbances with a minimal impact
on system performance. Notwithstanding, network robustness assessment has been mostly …

Recent work, using electrical distance metrics and concepts from graph theory, has revealed
important results about the electrical connectivity of empiric power systems. Such structural …