Network theory concepts form the core of algorithms that are designed to uncover valuable
insights from various datasets. Especially, network centrality measures such as Eigenvector …

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

The abundance of massive network data in a plethora of applications makes scalable
analysis algorithms and software tools necessary to generate knowledge from such data in …

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 …

As a sustainable means of public transport, the safety of the urban rail transit is a significant
section of public safety and is highly important in urban sustainable development. Research …

Betweenness is a well-known centrality measure that ranks the nodes according to their
participation in the shortest paths of a network. In several scenarios, having a high …

People use online social networks to exchange information, spread ideas, learn about
innovations, etc. Thus, it is important to know how information spreads through social …

The harmonic closeness centrality measure associates, to each node of a graph, the
average of the inverse of its distances from all the other nodes (by assuming that …

The closeness centrality of a vertex in a classical static graph is the reciprocal of the sum of
the distances to all other vertices. However, networks are often dynamic and change over …

The study of vertex centrality measures is a key aspect of network analysis. Naturally, such
centrality measures have been generalized to groups of vertices; for popular measures it …