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

Big graphs (networks) arising in numerous application areas pose significant challengesfor
graph analysts as these graphs grow to billions of nodes and edges and are prohibitively …

The risk prediction in the software development is mandatory for it to be recognized,
categorized and prioritized earlier for the success of the project. The requirement gathering …

Counting triangles in a graph and incident to each vertex is a fundamental and frequently
considered task of graph analysis. We consider how to efficiently do this for huge graphs …

Network (Graph) is a powerful abstraction for representing underlying relations and
structures in large complex systems. Network visualization provides a convenient way to …

Finding the number of triangles in a graph (network) is an important problem in graph
analysis. The number of triangles also has important applications in graph mining. Big …

Louvain algorithm is a well-known and efficient method for detecting communities or clusters
in social and information networks (graphs). The emergence of large network data …

Triangle enumeration is a foundation brick for solving harder graph problems related to
social networks, the Internet and transportation, to name a few applications. This problem is …

Community detection is an important graph (network) analysis kernel used for discovering
functional units and organization of a graph. Louvain method is an efficient algorithm for …

Distributed-memory triangle enumerating has attracted considerable interests due to its
potential capability to process huge graphs quickly. However, existing algorithms suffer from …