Clustering is an unsupervised learning technology, and it groups information (observations
or datasets) according to similarity measures. Developing clustering algorithms is a hot topic …

Core-periphery structure, the arrangement of a network into a dense core and sparse
periphery, is a versatile descriptor of various social, biological, and technological networks …

In the context of landscape fragmentation, a variety of ecological conservation theories have
been proposed to maintain habitat connectivity. These theories focus on the connectivity of …

The most common machine-learning methods solve supervised and unsupervised problems
based on datasets where the problem's features belong to a numerical space. However …

Ecological connectivity is the foundation of maintaining urban biodiversity and ecosystem
health. Identifying and managing ecological (connectivity) networks can help maintain the …

The most widely used techniques for community detection in networks, including methods
based on modularity, statistical inference, and information theoretic arguments, all work by …

Community detection methods attempt to divide a network into groups of nodes that share
similar properties, thus revealing its large-scale structure. A major challenge when …

Normalized mutual information is widely used as a similarity measure for evaluating the
performance of clustering and classification algorithms. In this paper, we show that results …

We study the problem of clustering networks whose nodes have imputed or physical
positions in a single dimension, for example prestige hierarchies or the similarity dimension …

Fraud in public funding can have deleterious consequences for societies' economic, social,
and political well-being. Fraudulent activity associated with public procurement contracts …