Betweenness centrality (BC) is a crucial graph problem that measures the significance of a
vertex by the number of shortest paths leading through it. We propose Maximal Frontier …

Network science methodology is increasingly applied to a large variety of real-world
phenomena, often leading to big network data sets. Thus, networks (or graphs) with millions …

We present Min-Rounds BC (MRBC), a distributed-memory algorithm in the CONGEST
model that computes the betweenness centrality (BC) of every vertex in a directed …

Experts from several disciplines have been widely using centrality measures for analyzing
large as well as complex networks. These measures rank nodes/edges in networks by …

The exploration and analysis of Web graphs has flourished in the recent past, producing a
large number of relevant and interesting research results. However, the unique …

Background Many algorithms require doing a large number of betweenness centrality
calculations quickly, and accommodating this need is an active open research area. There …

Efficient HPC libraries often expose multiple tunable parameters, algorithmic
implementations, or a combination of them, to provide optimized routines. The optimal …

Abstract The Critical Node Detection Problem (CNDP) consists in finding the set of nodes,
defined critical, whose removal maximally degrades the graph. In this work we focus on …

Searching and retrieving information from the Web is a primary activity needed to monitor
the development and usage of Web resources. Possible benefits include improving user …

Betweenness centrality is one of the most popular vertex centrality measures in network
analysis. Hence, many (sequential and parallel) algorithms to compute or approximate …