Incremental Algorithms for Network Management and Analysis based on Closeness Centrality
Abstract
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 centralitybased 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 reallife networks, especially on smallworld networks, which have a small diameter and a spikeshaped shortest distance distribution. In addition to closeness centrality, they can also be a great arsenal for the shortestpathbased 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 DBLPcoauthorship 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 largescale network management based on closeness centrality values.
 Publication:

arXiv eprints
 Pub Date:
 March 2013
 arXiv:
 arXiv:1303.0422
 Bibcode:
 2013arXiv1303.0422E
 Keywords:

 Computer Science  Data Structures and Algorithms