Correlated Growth of Causal Networks

TL;DR

This paper introduces a growth model for causal networks that explains how topological correlations naturally emerge, supported by analytical derivations and empirical validation across large-scale innovation networks.

Contribution

It presents a novel theoretical framework for understanding correlated growth in causal networks, linking degree correlations to causal and dynamic dependencies.

Findings

Degree correlations arise from marginal dependencies on causal and dynamic correlations.

Theoretical predictions match empirical data from four large-scale innovation networks.

Provides a general framework for understanding correlated growth in causal systems.

Abstract

The study of causal structure in complex systems has gained increasing attention, with many recent studies exploring causal networks that capture cause-effect relationships across diverse fields. Despite increasing empirical evidence linking causal structures to network topological correlations, the mechanisms underlying the emergence of these correlations in causal networks remain poorly understood. In this work, we propose a general growth framework for causal networks, incorporating two key types of correlations: causal and dynamic. We analytically demonstrate that degree correlations emerge as a consequence of marginal dependencies on these correlations. Our theoretical predictions align quantitatively with empirical data from four large-scale innovation networks. Our theory not only sheds light on the origins of topological correlations but also provides a general framework for understanding correlated growth across causal systems.