Detecting clusters or communities in large graphs from the real world, such as the Amazon
dataset, information networks, and social networks, is of considerable interest. Extracting …

Community detection in social network analysis is crucial for understanding network
structure and organization. It helps identify cohesive groups of nodes, allowing for targeted …

Evaluating Community Detection Algorithms: A multidimensional issue Page 1 HAL Id: hal-01796539
https://hal.science/hal-01796539 Submitted on 20 May 2018 HAL is a multi-disciplinary …

Community detection has attracted many attentions recently. Considering the effect of
current network structure on the result of the recent community detection methods is useful to …

One of the most relevant and widely studied structural properties of networks is their
community structure or clustering. Detecting communities is of great importance in various …

Detecting the underlying community structure of networks is an important problem in
complex network analysis. Modularity is a well-known quality function introduced by …

Community detection is a very popular research topic in network science nowadays. Various
categories of community detection algorithms have been proposed, such as graph …

Community detection in networks is used to understand the underlying structure of the
network and obtain insight into the structure of the network. Evaluation of the detected …

Understanding community structure in social media is critical due to its broad applications
such as friend recommendations, link predictions and collaborative filtering. However, there …

Modularity-based algorithms used for community detection have been increasing in recent
years. Modularity and its application have been generating controversy since some authors …