DeepAI AI Chat
Log In Sign Up

Hybrid Power-Law Models of Network Traffic

by   Pat Devlin, et al.

The availability of large scale streaming network data has reinforced the ubiquity of power-law distributions in observations and enabled precision measurements of the distribution parameters. The increased accuracy of these measurements allows new underlying generative network models to be explored. The preferential attachment model is a natural starting point for these models. This work adds additional model components to account for observed phenomena in the distributions. In this model, preferential attachment is supplemented to provide a more accurate theoretical model of network traffic. Specifically, a probabilistic complex network model is proposed using preferential attachment as well as additional parameters to describe the newly observed prevalence of leaves and unattached nodes. Example distributions from this model are generated by considering random sampling of the networks created by the model in such a way that replicates the current data collection methods.


page 1

page 2

page 3

page 4


From the power law to extreme value mixture distributions

The power law is useful in describing count phenomena such as network de...

Bayesian inference on random simple graphs with power law degree distributions

We present a model for random simple graphs with a degree distribution t...

Assortativity and bidegree distributions on Bernoulli random graph superpositions

A probabilistic generative network model with n nodes and m overlapping ...

Power laws distributions in objective priors

The use of objective prior in Bayesian applications has become a common ...

On the approximation of queue-length distributions in transportation networks

This paper focuses on the analytical probabilistic modeling of vehicular...

New Phenomena in Large-Scale Internet Traffic

The Internet is transforming our society, necessitating a quantitative u...

Hierarchical network models for structured exchangeable interaction processes

Network data often arises via a series of structured interactions among ...