Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …

Performing an accurate clustering analysis is of great significance for us to understand the
behavior of complex networks, and a variety of graph clustering algorithms have, thus, been …

Attributed graph (AG) clustering is a fundamental, yet challenging, task for studying
underlying network structures. Recently, a variety of graph representation learning models …

Abstract In this Point of View, we review a number of recent discoveries from the emerging,
interdisciplinary field of Network Science, which uses graph theoretic techniques to …

The growing number of applications of machine learning and data mining in many domains—
from agriculture to business, education, industrial manufacturing, and medicine—gave rise …

Attribute-aware community discovery aims to find well-connected communities that are also
homogeneous wrt the labels carried by the nodes. In this work, we address such a …

Identifying protein complexes in protein-protein interaction (ppi) networks is often handled
as a community detection problem, with algorithms generally relying exclusively on the …

Heterogeneous information networks (HINs) refer to logical networks involving entities of
multiple types and their multiple relations, which are widely used for modeling real-world …

The growth of the Internet of Things (IoT) has caused an ever-increasing number of devices
to be added to the network. Fog computing is an emerging technology that aims to overcome …

In this paper we utilize an opportunity to construct ground truths for topics in the field of
atomic, molecular and optical physics. Our research questions in this paper focus on (i) how …