Algorithms that can be split into different pieces and executed on different processing devices and the result can be combined to form the final result.
Published in Chapter:
A Survey of Parallel Community Detection Algorithms
Sobin C. C. (IIT Roorkee, India), Vaskar Raychoudhury (IIT Roorkee, India), and Snehanshu Saha (PESIT-BSC, India)
Copyright: © 2017
|Pages: 26
DOI: 10.4018/978-1-5225-2498-4.ch001
Abstract
The amount of data generated by online social networks such as Facebook, Twitter, etc., has recently experienced an enormous growth. Extracting useful information such as community structure, from such large networks is very important in many applications. Community is a collection of nodes, having dense internal connections and sparse external connections. Community detection algorithms aim to group nodes into different communities by extracting similarities and social relations between nodes. Although, many community detection algorithms in literature, they are not scalable enough to handle large volumes of data generated by many of the today's big data applications. So, researchers are focusing on developing parallel community detection algorithms, which can handle networks consisting of millions of edges and vertices. In this article, we present a comprehensive survey of parallel community detection algorithms, which is the first ever survey in this domain, although, multiple papers exist in literature related to sequential community detection algorithms.