Graph clustering and community detection are central problems in modern data mining. The
increasing need for analyzing billion-scale data calls for faster and more scalable algorithms …

The study of complex networks has significantly advanced our understanding of community
structures which serves as a crucial feature of real-world graphs. Detecting communities in …

Designing a complex system generally requires its decomposition into smaller modular
constituents for the ease of design, integration, operation, and future upgrades. Typically …

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

Community detection is a vital task in many fields, such as social networks, and financial
analysis, to name a few. The Louvain method, the main workhorse of community detection …

Dynamic (temporal) graphs are a convenient mathematical abstraction for many practical
complex systems including social contacts, business transactions, and computer …

Distributed processing of large-scale graph data has many practical applications and has
been widely studied. In recent years, a lot of distributed graph processing frameworks and …

Graph Convolutional Network (GCN) has drawn considerable research attention in recent
times. Many different problems from diverse domains can be solved efficiently using GCN …

Background There is an increasing trend in using network science methods and algorithms,
including community detection methods, in different areas of healthcare. These areas …

Network (Graph) is a powerful abstraction for representing structures in large complex socio-
technological systems. Community detection reveals important patterns and structural …