Abstract
In massive networked systems, it is an important problem to obtain a certain kind of network statistic such as average degree or clustering coefficient. In this paper, we propose a one-shot scalable average degree estimation algorithm, which allows a monitoring node outside of the target network obtains the average degree with o(n) message complexity. The proposed algorithm is based on the method by Goldreich and Ron (GR), which is well-known in the context of property testing. In this sense our algorithm is a “network version” of it. While the original GR algorithm can be regarded as a pull-based scheme in the sense that the monitoring node can get information only from randomly chosen nodes, our algorithm utilizes push-based schemes, that is, each node in the target network can actively send information to the monitoring server. The primary contribution of this paper is that such push-based schemes actually yield better message complexity.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cormode, G., Muthukrishnan, S., Yi, K.: Algorithms for distributed functional monitoring. In: Proceedings of the Nineteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2008, pp. 1076–1085. Society for Industrial and Applied Mathematics, Philadelphia (2008)
Feige, U.: On sums of independent random variables with unbounded variance and estimating the average degree in a graph. SIAM Journal on Computing 35(4), 964–984 (2006)
Goldreich, O., Ron, D.: On estimating the average degree of a graph. Electronic Colloquim on Computational Complexity, ECCC (2004)
Motwani, R., Panigrahy, R., Xu, Y.: Estimating sum by weighted sampling. In: Arge, L., Cachin, C., Jurdziński, T., Tarlecki, A. (eds.) ICALP 2007. LNCS, vol. 4596, pp. 53–64. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Izumi, T., Kanzaki, H. (2013). Scalable Estimation of Network Average Degree. In: Higashino, T., Katayama, Y., Masuzawa, T., Potop-Butucaru, M., Yamashita, M. (eds) Stabilization, Safety, and Security of Distributed Systems. SSS 2013. Lecture Notes in Computer Science, vol 8255. Springer, Cham. https://doi.org/10.1007/978-3-319-03089-0_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-03089-0_32
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03088-3
Online ISBN: 978-3-319-03089-0
eBook Packages: Computer ScienceComputer Science (R0)