Qube a quick algorithm for updating betweenness centrality
Qube a quick algorithm for updating betweenness centrality - date in asia dating site
It is a measure which quantifies the importance of a vertex based on its occurrence in shortest paths between all possible pairs of vertices in a graph.
A classification for community discovery methods in complex networks. Community detection in large-scale networks: A Survey and empirical evaluation. Most of the algorithms that are used to find betwenness centrality assume the constancy of the graph and are not efficient for .We propose a technique to update betweenness centrality of a graph when nodes are added or deleted.Besides that, recent years have seen the publication of dynamic algorithms for efficient recomputation of betweenness in evolving networks.In previous work we proposed the first semi-dynamic algorithms that recompute an of betweenness in connected graphs after batches of edge insertions.In this paper we propose the first fully-dynamic approximation algorithms (for weighted and unweighted undirected graphs that need not to be connected) with a provable guarantee on the maximum approximation error.
The transfer to fully-dynamic and disconnected graphs implies additional algorithmic problems that could be of independent interest.
The Betweenness centrality is widely used in network analyses.
Especially in a social network, the recursive computation of the betweenness centralities of vertices is performed for the community detection and finding the influential user in the network.
In addition, we extend our former algorithm for semi-dynamic BFS to batches of both edge insertions and deletions.
Using approximation, our algorithms are the first to make in-memory computation of betweenness in fully-dynamic networks with millions of edges feasible.
Our experiments show that they can achieve substantial speedups compared to recomputation, up to several orders of magnitude.