Community detection is one of the popular research topics in the field of complex networks
analysis. It aims to identify communities, represented as cohesive subgroups or clusters …

In this paper, we propose a community detection method to find regions in spatial data with
higher correlations. We construct a 'correlation deviation graph'that considers the influence …

Detecting core-periphery structures is one of the outstanding issues in complex network
analysis. Various algorithms can identify core nodes and periphery nodes. Recent advances …

It has been insufficiently explored how to perform density-based clustering by exploiting
textual attributes on social media. In this paper, we aim at discovering a social point-of …

We develop an agent-based model on a lattice to investigate territorial development
motivated by markings such as graffiti, generalizing a previously-published model to account …

A chain event graph (CEG) is a graphical model that is constructed by identifying the
probabilistic symmetries within the tree-based description of a process. CEGs generalise …

In recent years, community detection in dynamic networks has received great interest. Due
to its importance, many surveys have been suggested. In these surveys, the authors present …

Lens-free digital in-line holography (LDIH) produces cellular diffraction patterns (holograms)
with a large field of view that lens-based microscopes cannot offer. It is a promising …

When an earthquake causes shaking in a region, the amplitude of shaking varies spatially.
Ground motion models have been developed to predict the median and standard deviation …

This mixed methods study documents how the electronic nature of gang surveillance in
California (CalGang) facilitated the connection between data collected by local law …