Benchmarking measures of network controllability on canonical graph models

Abstract

Many real-world systems are composed of many individual components that interact with one another in a complex pattern to produce diverse behaviors. Understanding how to intervene in these systems to guide behaviors is critically important to facilitate new discoveries and therapies in systems biology and neuroscience. A promising approach to optimizing interventions in complex systems is network control theory, an emerging conceptual framework and associated mathematics to understand how targeted input to nodes in a network system can predictably alter system dynamics. While network control theory is currently being applied to real-world data, the practical performance of these measures on simple networks with pre-specified structure is not well understood. In this study, we benchmark measures of network controllability on canonical graph models, providing an intuition for how control strategy, graph topology, and edge weight distribution mutually depend on one another. Our numerical studies motivate future analytical efforts to gain a mechanistic understanding of the relationship between graph topology and control, as well as efforts to design networks with specific control profiles.

References in corpus (20)

• Finding and evaluating community structure in networks
• The spread of epidemic disease on networks
• Prediction and predictability of global epidemics: the role of the airline transportation network
• Controllability of Brain Networks
• Control Principles of Complex Networks
• Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems
• Controlling complex networks: How much energy is needed?
• Realistic Control of Network Dynamics
• Optimally controlling the human connectome: the role of network topology
• Controllability transition and nonlocality in network control
• The functional structure of cortical neuronal networks grown in vitro
• Developmental increases in white matter network controllability support a growing diversity of brain dynamics
• Topological Principles of Control in Dynamical Network Systems
• Traffic congestion in interconnected complex networks
• The Influence of Network Topology on Sound Propagation in Granular Materials
• Evolution of network architecture in a granular material under compression
• Resolving structural variability in network models and the brain
• Networkcontrology
• Emergence of assortative mixing between clusters of cultured neurons
• Embedding of biological distribution networks with differing environmental constraints

Cited by in corpus (10)

• Control of Dynamics in Brain Networks
• Time-evolving controllability of effective connectivity networks during seizure progression
• Predicting Network Controllability Robustness: A Convolutional Neural Network Approach
• Warnings and Caveats in Brain Controllability
• Knowledge-Based Prediction of Network Controllability Robustness
• Towards Optimal Robustness of Network Controllability: An Empirical Necessary Condition
• The control of brain network dynamics across diverse scales of space and time
• Controllability Analysis of Functional Brain Networks
• Network structure of cascading neural systems predicts stimulus propagation and recovery
• Controllability maximization of large-scale systems using projected gradient method