In recent years, significant advances have been made in the design and analysis of fully
dynamic algorithms. However, these theoretical results have received very little attention …

In recent years, significant advances have been made in the design and analysis of fully
dynamic algorithms. However, these theoretical results have received very little attention …

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 …

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 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 …

In many modern applications, the generated data is a dynamic network. These networks are
graphs that change over time by a sequence of update operations (node addition, node …

We consider the problem of efficiently computing the top-k temporal closeness values and
the corresponding vertex sets in a given temporal network. The closeness centrality of a …

Temporal graphs are often good models for real-life scenarios due to the inherently dynamic
nature of most real-world activities and processes. A significant difference between …

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 …

Centrality measures characterize important nodes in networks. Efficiently computing such
nodes has received a lot of attention. When considering the generalization of computing …