Fast centrality approximation in modular networks

Shu Yan Chan, Ian X.Y. Leung, Pietro Liò
2009 Proceeding of the 1st ACM international workshop on Complex networks meet information & knowledge management - CNIKM '09  
Measuring the centrality of nodes in real-world networks has remained an important task in the technological, social, and biological network paradigms carrying implications on their analysis and applications. Exact inference of centrality values is infeasible in large networks due to the need to solve the all-pairs shortest path problem. We introduce a framework to approximate node centralities in real-world networks that are known to exhibit modularity, i.e., the presence of dense subgraphs or
more » ... communities, which are themselves sparsely connected. We also propose a novel centrality measure known as Community Inbetweenness that ranks nodes based solely on community information. In a modular network of size n with √ n evenly sized communities and m edges, our framework requires linear time O(m) and O( √ nm) time for the approximation of closeness and betweenness respectively. Utilizing a recently proposed linear time method in community detection, our approximation techniques are faster than traditional sampling algorithms, applicable in real-time distributed environments, and offer highly comparable results.
doi:10.1145/1651274.1651282 dblp:conf/cikm/ChanLL09 fatcat:6bllqdqhazha7mx7ichmppgvl4