MR-IBC: MapReduce-based incremental betweenness centrality in large-scale complex networks

Abstract

With the increase in popularity of the social networks, it has become a perennial source of data analytics to mining the abundant information in real-world networks. As the social network is heterogeneous, some of its entities like nodes and edges may be more influential than other entities. It is observed that identification of the most influential entities in large-scale networks has many real-time applications like social network analysis, fraud detection, community detection, traffic control of the transportation network, software-defined network, and many more. Several centrality measures exist to identify the importance of a node in the network. However, betweenness centrality is found to be the most promising one, to investigate a network and the importance of nodes in the network. In the era of big data, the size of the social network is increasing exponentially. Although a number of algorithms exist for identifying betweenness centrality for large-scale networks, very few algorithms attempt to identify the influential nodes in a dynamic network. A single insertion or deletion of a node or edge may drastically change the structure of the network, which limits the performance of algorithms in terms of computational efficiency. In order to accommodate this problem, in this paper, a MapReduce-based incremental parallel algorithm for exploring the influential nodes based on betweenness centrality is proposed. The proposed algorithm is compared with a few other algorithms that are used to compute betweenness centrality in a dynamic network. The major advantage of the proposed algorithm is that it allows the network to be updated by the insertion of an edge. The effectiveness of the proposed algorithm has been critically examined in a distributed environment in terms of execution time by using both real-time and synthetic networks. The experimental results show a significant speedup over the other algorithms.