  Analyzing networks requires complex algorithms to extract meaningful
information. Centrality metrics have shown to be correlated with the importance
and loads of the nodes in network traffic. Here, we are interested in the
problem of centrality-based network management. The problem has many
applications such as verifying the robustness of the networks and controlling
or improving the entity dissemination. It can be defined as finding a small set
of topological network modifications which yield a desired closeness centrality
configuration. As a fundamental building block to tackle that problem, we
propose incremental algorithms which efficiently update the closeness
centrality values upon changes in network topology, i.e., edge insertions and
deletions. Our algorithms are proven to be efficient on many real-life
networks, especially on small-world networks, which have a small diameter and a
spike-shaped shortest distance distribution. In addition to closeness
centrality, they can also be a great arsenal for the shortest-path-based
management and analysis of the networks. We experimentally validate the
efficiency of our algorithms on large networks and show that they update the
closeness centrality values of the temporal DBLP-coauthorship network of 1.2
million users 460 times faster than it would take to compute them from scratch.
To the best of our knowledge, this is the first work which can yield practical
large-scale network management based on closeness centrality values.
