Estimating time-varying networks

TL;DR

This paper introduces two novel machine learning methods for estimating dynamic, time-varying networks from time series data, using a scalable, regularized logistic regression framework, demonstrated on simulated and real biological and political data.

Contribution

The paper presents two new methods for estimating time-varying networks based on a scalable, regularized logistic regression approach, addressing a gap in dynamic network inference.

Findings

Successfully recovered simulated time-varying networks.

Reconstructed political networks from US Senate voting records.

Inferred regulatory networks in Drosophila across its life cycle.

Abstract

Stochastic networks are a plausible representation of the relational information among entities in dynamic systems such as living cells or social communities. While there is a rich literature in estimating a static or temporally invariant network from observation data, little has been done toward estimating time-varying networks from time series of entity attributes. In this paper we present two new machine learning methods for estimating time-varying networks, which both build on a temporally smoothed $l_1$-regularized logistic regression formalism that can be cast as a standard convex-optimization problem and solved efficiently using generic solvers scalable to large networks. We report promising results on recovering simulated time-varying networks. For real data sets, we reverse engineer the latent sequence of temporally rewiring political networks between Senators from the US Senate voting records and the latent evolving regulatory networks underlying 588 genes across the life cycle of Drosophila melanogaster from the microarray time course.