Community detection (or clustering) in large-scale graphs is an important problem in graph
mining. Communities reveal interesting organizational and functional characteristics of a …

Finding cohesive subgraphs is a crucial graph analysis kernel widely used for social and
biological networks (graphs). There exist various approaches for discovering insightful …

Community detection has become an important graph analysis kernel due to the
tremendous growth of social networks and genomics discoveries. Even though there exist a …

Community detection is an essential task in network analysis as it helps identify groups and
patterns within a network. High-speed community detection algorithms are necessary to …

Information-theoretic community discovery method (popularly known as Infomap) is known
for delivering better quality results in the Lancichinetti–Fortunat–Radicchi (LFR) benchmark …

Identifying Community structures is a fundamental problem in graph analysis. To detect
communities in massive contemporary graphs, researchers have extensively explored …

A graph consists of objects, called nodes, that are connected by edges. These connections
may have a certain strength or a direction. Graphs can be used in a lot of contexts where …

Community detection is a key aspect of network analysis, as it allows for the identification of
groups and patterns within a network. With the ever-increasing size of networks, it is crucial …

Background and Objectives: Louvain is a time-consuming community detection algorithm
especially in large-scale networks. Using Graphic Processing Unit (GPU) in order to …

Parallel computing plays a crucial role in processing large-scale graph data. Complex
network analysis is an exciting area of research for many applications in different scientific …