Betweenness centrality updation and community detection in streaming graphs using incremental algorithm

TLDR: This paper computed betweenness centrality by identifying communities lying within the network by efficiently updates the centrality of the nodes whenever any edge or vertex addition or deletion takes place within the dynamic network by modifying solely a subset of vertices.

Abstract: Centrality measures have perpetually been helpful to find the foremost central or most powerful node within the network. There are numerous strategies to compute centrality of a node however in social networks betweenness centrality is the most widely used approach to bifurcate communities within the network, to find out the susceptibility within the complex networks and to generate the scale free networks whose degree distribution follows the power law. In this paper, we've computed betweenness centrality by identifying communities lying within the network. Our algorithm efficiently updates the centrality of the nodes whenever any edge or vertex addition or deletion takes place within the dynamic network by modifying solely a subset of vertices. For the vertex addition, Incremental Algorithm has been used in which Streaming graphs has also been considered. Brandes approach is the most widely used approach for finding out the betweenness centrality however it's still expensive for growing networks since it takes O(mn+n2logn) amount of time and O(n+m) space however our approach efficiently updates the centrality of the nodes by taking O(|S|n+|S|nlogn) amount of time where |S| is the subset of the vertices,m is the number of edges, n is the number of vertices and |S|≤n holds true.