Reconstructing dynamical networks via feature ranking

Abstract

Empirical data on real complex systems are becoming increasingly available. Parallel to this is the need for new methods of reconstructing (inferring) the topology of networks from time-resolved observations of their node-dynamics. The methods based on physical insights often rely on strong assumptions about the properties and dynamics of the scrutinized network. Here, we use the insights from machine learning to design a new method of network reconstruction that essentially makes no such assumptions. Specifically, we interpret the available trajectories (data) as features, and use two independent feature ranking approaches -- Random forest and RReliefF -- to rank the importance of each node for predicting the value of each other node, which yields the reconstructed adjacency matrix. We show that our method is fairly robust to coupling strength, system size, trajectory length and noise. We also find that the reconstruction quality strongly depends on the dynamical regime.

References in corpus (27)

• Scikit-learn: Machine Learning in Python
• Machine Learning in Automated Text Categorization
• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• Synchronization in complex networks
• Characterization of complex networks: A survey of measurements
• Critical phenomena in complex networks
• Synchronization reveals topological scales in complex networks
• Representation of compounds for machine-learning prediction of physical properties
• Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems
• Quantum Loop Topography for Machine Learning
• Machine Learning for Precise Quantum Measurement
• Revealing networks from dynamics: an introduction
• Model-free inference of direct network interactions from nonlinear collective dynamics
• Combining complex networks and data mining: why and how
• Inference of Time-Evolving Coupled Dynamical Systems in the Presence of Noise
• Direct coupling information measure from non-uniform embedding
• Random Delays and the Synchronization of Chaotic Maps
• Network reconstruction from random phase-resetting
• Revealing physical interaction networks from statistics of collective dynamics
• Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition
• Reconstructing networks of pulse-coupled oscillators from spike trains
• Successful network inference from time-series data using Mutual Information Rate
• Reconstruction of chaotic neural network from observed firing rates
• Phase space reconstruction for non-uniformly sampled noisy time series
• Extracting topological features from dynamical measures in networks of Kuramoto oscillators
• Synchronizability of chaotic logistic maps in delayed complex networks
• Decoupling approximation robustly reconstructs directed dynamical networks

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• Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Tests
• Impact of network characteristics on network reconstruction
• Iterative procedure for network inference