Betweenness centrality is a fundamental measure in social network analysis, expressing the
importance or influence of individual vertices in a network in terms of the fraction of shortest …

ABPA Ξ A Σ (ABRAXAS): Gnostic word of mystic meaning. We present ABRA, a suite of
algorithms to compute and maintain probabilistically guaranteed high-quality …

Graph-processing platforms are increasingly used in a variety of domains. Although both
industry and academia are develo** and tuning graph-processing algorithms and …

We present KADABRA, a new algorithm to approximate betweenness centrality in directed
and undirected graphs, which significantly outperforms all previous approaches on real …

The betweenness centrality metric has always been intriguing for graph analyses and used
in various applications. Yet, it is one of the most computationally expensive kernels in graph …

Centrality metrics have shown to be highly correlated with the importance and loads of the
nodes within the network traffic. In this work, we provide fast incremental algorithms for …

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 …

Centrality measures allow to measure the relative importance of a node or an edge in a
graph wrt~ other nodes or edges. Several measures of centrality have been developed in …

The betweenness and closeness metrics are widely used metrics in many network analysis
applications. Yet, they are expensive to compute. For that reason, making the betweenness …

Betweenness Centrality (BC) has proven to be a fundamental metric in many domains to
identify the components (nodes) of a system modelled as a graph that are mostly traversed …