Over the past few years, the number and volume of data sources in healthcare databases
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …

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

The most basic and significant issue in complex network analysis is community detection,
which is a branch of machine learning. Most current community detection approaches, only …

The diffusion method is one of the main methods of community detection in complex
networks. In this method, the use of the concept that diffusion within the nodes that are …

Attributed graph clustering, the task of grouping nodes into communities using both graph
structure and node attributes, is a fundamental problem in graph analysis. Recent …

The COVID-19 pandemic has produced a flurry of online activity on social media sites. As
such, analysis of social media data during the COVID-19 pandemic can produce unique …

Community detection is one way to reduce the complexity of analyzing networks, especially
with their rapid growth. Dividing networks into communities can help analysts and experts to …

Complex networks have a large number of nodes and edges, which prevents the
understanding of network structure and the discovery of valid information. This paper …

Community detection is an important problem in network science that discovers highly
clustered groups of nodes having similar properties. Label propagation algorithm (LPA) is …

Community detection in social networks is becoming one of the key tasks in social network
analysis, since it helps analyzing groups of users with similar interests. This task is also …