Online Social Networks (OSNs) are becoming a popular method of meeting people and
keeping in touch with friends. OSNs resort to trust evaluation models and algorithms to …

Community structure is ubiquitous in real-world complex networks. The task of community
detection over these networks is of paramount importance in a variety of applications …

Given a large network, local community detection aims at finding the community that
contains a set of query nodes and also maximizes (minimizes) a goodness metric. This …

Hypergraphs are a natural and powerful choice for modeling group interactions in the real
world, which are often referred to as higher-order networks. For example, when modeling …

These days, community detection is an important field to understand the topology and
functions in the complex networks. In this article, we propose a novel Community Detection …

Community detection is expensive, and the cost generally depends at least linearly on the
number of vertices in the graph. We propose working with a reduced graph that has many …

Degree assortativity characterizes the propensity for large-degree nodes to connect to other
large-degree nodes and low-degree to low-degree. It is important to describe the forces …

Local community detection is a widely used method for identifying groups of nodes starting
from seeding nodes. The seed (s) are usually selected either randomly or based only on …

Modularity maximization is one of the state-of-the-art methods for community detection that
has gained popularity in the last decade. Yet it suffers from the resolution limit problem by …

Community detection, which focuses on clustering vertex interactions, plays a significant role
in network analysis. However, it also faces numerous challenges like missing data and …