Kernel Granger causality and the analysis of dynamical networks

TL;DR

This paper introduces a kernel-based multivariate Granger causality method for analyzing dynamical networks, capable of capturing nonlinear interactions and reconstructing network topology from time series data.

Contribution

It extends kernel Granger causality to multivariate cases, addressing false causality issues and allowing network topology reconstruction without assuming acyclic graphs.

Findings

Successfully reconstructed network topology from simulated data.

Identified causal relationships in gene expression data related to tumor development.

Compared bivariate and multivariate methods, showing advantages of the proposed approach.

Abstract

We propose a method of analysis of dynamical networks based on a recent measure of Granger causality between time series, based on kernel methods. The generalization of kernel Granger causality to the multivariate case, here presented, shares the following features with the bivariate measures: (i) the nonlinearity of the regression model can be controlled by choosing the kernel function and (ii) the problem of false-causalities, arising as the complexity of the model increases, is addressed by a selection strategy of the eigenvectors of a reduced Gram matrix whose range represents the additional features due to the second time series. Moreover, there is no {\it a priori} assumption that the network must be a directed acyclic graph. We apply the proposed approach to a network of chaotic maps and to a simulated genetic regulatory network: it is shown that the underlying topology of the network can be reconstructed from time series of node's dynamics, provided that a sufficient number of samples is available. Considering a linear dynamical network, built by preferential attachment scheme, we show that for limited data use of bivariate Granger causality is a better choice w.r.t methods using $L1$ minimization. Finally we consider real expression data from HeLa cells, 94 genes and 48 time points. The analysis of static correlations between genes reveals two modules corresponding to well known transcription factors; Granger analysis puts in evidence nineteen causal relationships, all involving genes related to tumor development.