which represent the evolution of a target graph.
Such models have long
been a topic of interest for a number of networks, especially
communications networks. The solution developed in this paper
gives a rigorous way to calculate the likelihood of the observed graph evolution
having arisen from a wide variety of hypothesised models encompassing
many already present in the literature. The framework is
shown to recover parameters from artificial data and is tested
on real data sets from Facebook and from emails from the company Enron.
This paper used a likelihood based framework to create a rigorous way to assess models of networks. Network evolution is broken down into an operation model (it decides the 'type' of change to be made to the network, e.g. "add node" "add link" "remove node" "remove link") and an object model (that decides the exact change -- which node/link to add).
The system is shown to be able to recover known parameters on artificial models and to be useful in analysis of real data.
This work can generate graphs from a very large family with the aim of fitting those graph to parameters of real data sets.
title= "Likelihood-based assessment of dynamic networks",
journal= "Journal of Complex Networks",
authors="Richard Clegg and Ben Parker and Miguel Rio"