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Qube a quick algorithm for updating betweenness centrality

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With the aim of filling this vital gap, we propose a fast solution using CUDA for calculating the BC in large weighted networks based on previous GPU BC algorithms and SSSP algorithms.

To make our algorithm more efficient, we make efforts to optimize it by employing several novel techniques to overcome the influence of irregular network structures.

Although several parallel algorithms have been presented to reduce its calculation cost for unweighted networks, a fast solution for weighted networks, which are commonly encountered in many realistic applications, is still lacking.

qube a quick algorithm for updating betweenness centrality-86qube a quick algorithm for updating betweenness centrality-56qube a quick algorithm for updating betweenness centrality-69

In weighted networks, the Dijkstra algorithm should be used to solve the single-source shortest path (SSSP) problem rather than the breadth-first search (BFS) algorithm.

At the same time, the majority of the nodes do not need to be inspected in each step; hence, processing all vertices simultaneously, as is done in traditional algorithms, is wasteful.

Mc Laughlin & Bader (2014) proposed a work-efficient strategy to overcome this problem.

CUDA, developed by Nvidia Corporation, is the most popular GPU computing framework, and some researchers have even used this framework to parallelize the Brandes algorithm (Shi & Zhang, 2011; Sariyüce et al., 2013; Mc Laughlin & Bader, 2014, 2015).

However, previous works have concentrated on unweighted networks for simplicity, although to the best of our knowledge, many realistic networks are weighted ones.