Maximum likelihood estimation for mechanistic network models

by   Jonathan Larson, et al.

Mechanistic network models specify the mechanisms by which networks grow and change, allowing researchers to investigate complex systems using both simulation and analytical techniques. Unfortunately, it is difficult to write likelihoods for instances of graphs generated with mechanistic models because of a combinatorial explosion in outcomes of repeated applications of the mechanism. Thus it is near impossible to estimate the parameters using maximum likelihood estimation. In this paper, we propose treating node sequence in a growing network model as an additional parameter, or as a missing random variable, and maximizing over the resulting likelihood. We develop this framework in the context of a simple mechanistic network model, used to study gene duplication and divergence, and test a variety of algorithms for maximizing the likelihood in simulated graphs. We also run the best-performing algorithm on a human protein-protein interaction network and four non-human protein-protein interaction networks. Although we focus on a specific mechanistic network model here, the proposed framework is more generally applicable to reversible models.



There are no comments yet.


page 15


Improved Maximum Likelihood Estimation of ARMA Models

In this paper we propose a new optimization model for maximum likelihood...

Maximum Likelihood Estimation of Sparse Networks with Missing Observations

Estimating the matrix of connections probabilities is one of the key que...

Fast Maximum Likelihood estimation via Equilibrium Expectation for Large Network Data

Complex network data may be analyzed by constructing statistical models ...

Invariant theory and scaling algorithms for maximum likelihood estimation

We show that maximum likelihood estimation in statistics is equivalent t...

Maximum likelihood estimation of hidden Markov models for continuous longitudinal data with missing responses and dropout

We propose an inferential approach for maximum likelihood estimation of ...

Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry

Deep neural networks have achieved state of the art accuracy at classify...

Model-based clustering for populations of networks

We propose a model-based clustering method for populations of networks t...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.