Snapshot Centrality Indices in Dynamic FIFO Networks

Authors:
Tatiana M. Tabirca;Kenneth N. Brown;Cormac J. Sreenan
Affiliations:
Mobile and Internet System Laboratory, Department of Computer Science, University of College Cork, Cork, Ireland;Cork Constraint Computation Centre, Department of Computer Science, University of College Cork, Cork, Ireland;Mobile and Internet System Laboratory, Department of Computer Science, University of College Cork, Cork, Ireland
Venue:
Journal of Mathematical Modelling and Algorithms
Year:
2011

Citing 8
Cited 0

Network flows: theory, algorithms, and applications

Network flows: theory, algorithms, and applications
An incremental algorithm for a generalization of the shortest-path problem

Journal of Algorithms
Fully dynamic all pairs shortest paths with real edge weights

Journal of Computer and System Sciences - Special issue on FOCS 2001
Emergency Evacuation using Wireless Sensor Networks

LCN '07 Proceedings of the 32nd IEEE Conference on Local Computer Networks
A Dynamic Model for Fire Emergency Evacuation Based on Wireless Sensor Networks

ISPDC '09 Proceedings of the 2009 Eighth International Symposium on Parallel and Distributed Computing
Adaptive Networks: Theory, Models and Applications

Adaptive Networks: Theory, Models and Applications
Computational Social Network Analysis: Trends, Tools and Research Advances

Computational Social Network Analysis: Trends, Tools and Research Advances
Centrality metric for dynamic networks

Proceedings of the Eighth Workshop on Mining and Learning with Graphs

Quantified Score

Hi-index	0.00

Visualization

Abstract

The article introduces the concept of snapshot dynamic indices as centrality measures to analyse how the importance of nodes changes over time in dynamic networks. In particular, the dynamic stress-snapshot and dynamic betweenness snapshot are investigated. We present theoretical results on dynamic shortest paths in first-in first-out dynamic networks, and then introduce some algorithms for computing these indices in the discrete-time case. Finally, we present some experimental results exploring the algorithms' efficiency and illustrating the variation of the dynamic betweenness snapshot index for some sample dynamic networks.