Community detection, also known as graph clustering, has been extensively studied in the
literature. The goal of community detection is to partition vertices in a complex graph into …

A multiplex network models different modes of interaction among same-type entities. In this
article, we provide a taxonomy of community detection algorithms in multiplex networks. We …

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas
such as security, finance, health care, and law enforcement. While numerous techniques …

Multilayer networks, in particular multilayer social networks, where users belong to and
interact on different networks at the same time, are an active research area in social network …

Clustering a graph, ie, assigning its nodes to groups, is an important operation whose best
known application is the discovery of communities in social networks. Graph clustering and …

Community discovery has emerged during the last decade as one of the most challenging
problems in social network analysis. Many algorithms have been proposed to find …

Given a co-authorship collaboration network, how well can we cluster the participating
authors into communities? If we also consider their citation network, based on the same …

With the fast growth of smart devices and social networks, a lot of computing systems collect
data that record different types of activities. An important computational challenge is to …

With the advent of a wide spectrum of recent applications, querying heterogeneous
information networks (HINs) has received a great deal of attention from both academic and …

Multi-layer networks provide an effective and efficient tool to model and characterize
complex systems with multiple types of interactions, which differ greatly from the traditional …