Advancing network resilience theories with symbolized reinforcement learning

TL;DR

This paper introduces an automatic, AI-driven method to discover network resilience theories that incorporate both topology and dynamics, providing more accurate and insightful models for preventing systemic failures.

Contribution

It presents the first resilience theory that accounts for both network topology and dynamics, using symbolized reinforcement learning to improve understanding and prediction of network failures.

Findings

Discovered a new resilience theory combining topology and dynamics.

Achieved over 37.5% improvement in accuracy of existing resilience models.

Provided insights for early warning signals of network collapse.

Abstract

Many complex networks display remarkable resilience under external perturbations, internal failures and environmental changes, yet they can swiftly deteriorate into dysfunction upon the removal of a few keystone nodes. Discovering theories that measure network resilience offers the potential to prevent catastrophic collapses--from species extinctions to financial crise--with profound implications for real-world systems. Current resilience theories address the problem from a single perspective of topology, neglecting the crucial role of system dynamics, due to the intrinsic complexity of the coupling between topology and dynamics which exceeds the capabilities of human analytical methods. Here, we report an automatic method for resilience theory discovery, which learns from how AI solves a complicated network dismantling problem and symbolizes its network attack strategies into theoretical formulas. This proposed self-inductive approach discovers the first resilience theory that accounts for both topology and dynamics, highlighting how the correlation between node degree and state shapes overall network resilience, and offering insights for designing early warning signals of systematic collapses. Additionally, our approach discovers formulas that refine existing well-established resilience theories with over 37.5% improvement in accuracy, significantly advancing human understanding of complex networks with AI.