Detecting and analyzing dense groups or communities from social and information networks
has attracted immense attention over the last decade due to its enormous applicability in …

Matrix tri-factorization subject to binary constraints is a versatile and powerful framework for
the simultaneous clustering of observations and features, also known as biclustering …

Normalized mutual information (NMI) is a widely used measure to compare community
detection methods. Recently, however, the need of adjustment for information theory‐based …

The legal field is generally burdened by paper-heavy activities, and the management of
massive amounts of legal judgments without the adoption of computational tools may …

How can we accurately compare different community detection algorithms? These
algorithms cluster nodes in a given network, and their performance is often validated on …

Recurrent or non-recurrent temperature and humidity variations trigger various damages on
the inside and outside surfaces of buildings, which eventually lead to poor insulation …

Abstract Overlapping Community Detection from a real network is unsupervised, and it is
hard to know the exact community number or quantized strength of every node related to …

Clustering is a fundamental problem in data science, yet the variety of clustering methods
and their sensitivity to parameters make clustering hard. To analyze the stability of a given …

If Alice is majoring in Computer Science, can we guess the major of her friend Bob? Even
harder, can we determine Bob's age or sexual orientation? Attributed graphs are ubiquitous …

The main aim of a coal deposit model is to provide an effective basis for mine production
planning. The most applied approach is related to block modeling as a reasonable global …