# Qube a quick algorithm for updating betweenness centrality dating zambia russia

*26-Dec-2017 21:57*

Many graph computations, however, contain sufficient coarse grained parallelism for thousands of processors, which can be uncovered by using the right primitives.

We describe the Parallel Combinatorial BLAS, which consists of a small but powerful set of linear algebra primitives specifically targeting graph and data mining applications.

When a graph is changed, the betweenness centralities of all the vertices should be recomputed from scratch using all the vertices in the graph.

To the best of our knowledge, this is the first work that proposes an efficient algorithm which handles the update of the betweenness centralities of vertices in a graph.

We propose a technique to update betweenness centrality of a graph when nodes are added or deleted.

Our algorithm experimentally speeds up the calculation of betweenness centrality (after updation) from 7 to 412 times, for real graphs, in comparison to the currently best known technique to find betweenness centrality. R., Iyengar S., Sukrit (2013) A Faster Algorithm to Update Betweenness Centrality after Node Alteration.

Large combinatorial graphs appear in many applications of high-performance computing, including computational biology, informatics, analytics, web search, dynamical systems, and sparse matrix methods.

Experimental results on real graphs show that the proposed algorithm efficiently update betweenness centrality and detect communities in a graph.Citation Context ..to compute the exact betweenness centrality of all vertices by adapting the APSP Dijkstra algorithm.The algorithm runs in O(nm n 2 logn) time, which is prohibitive for large graphs. =-=[4]-=- gave a sampling-based approximation algorithm and showed that centrality is easier to approximate for central nodes.Betweenness centrality is a centrality measure that is widely used, with applications across several disciplines.

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.In the study of complex networks, a network is said to have community structure if the nodes of the network can be easily grouped into (potentially overlapping) sets of nodes such that each set of nodes is densely connected internally.