Evolutionary reconstruction of networks

Abstract

Can a graph specifying the pattern of connections of a dynamical network be reconstructed from statistical properties of a signal generated by such a system? In this model study, we present an evolutionary algorithm for reconstruction of graphs from their Laplacian spectra. Through a stochastic process of mutations and selection, evolving test networks converge to a reference graph. Applying the method to several examples of random graphs, clustered graphs, and small-world networks, we show that the proposed stochastic evolution allows exact reconstruction of relatively small networks and yields good approximations in the case of large sizes.

4 pages, 4 figures. Please, address all correspondence to [email protected]

References in corpus (1)

• Statistical mechanics of complex networks

Cited by in corpus (26)

• Network comparison and the within-ensemble graph distance
• Synchronization and structure in an adaptive oscillator network
• Spectral plots and the representation and interpretation of biological data
• netrd: A library for network reconstruction and graph distances
• Design of Easily Synchronizable Oscillator Networks Using the Monte Carlo Optimization Method
• Non-Markov stochastic dynamics of real epidemic process of respiratory infections
• Optimal network topologies for information transmission in active networks
• Design of Oscillator Networks with Enhanced Synchronization Tolerance against Noise
• A generative model for protein contact networks
• Assortative Mixing in Close-Packed Spatial Networks
• Laplacian Spectrum and Protein-Protein Interaction Networks
• Functional control of network dynamics using designed Laplacian spectra
• Thermodynamic Characterization of Synchronization-Optimized Oscillator-Networks
• Symmetry-based coarse-graining of evolved dynamical networks
• An introduction to spectral distances in networks (extended version)
• Biological network comparison via Ipsen-Mikhailov distance
• Network Distance Based on Laplacian Flows on Graphs
• Hypothesis Testing for Two Sample Comparison of Network Data
• Graph spectra as a systematic tool in computational biology
• Reconstruction of evolved dynamic networks from degree correlations
• Stability Indicators in Network Reconstruction
• DTW-MIC Coexpression Networks from Time-Course Data
• Inferring, comparing and exploring ecological networks from time-series data through R packages constructnet, disgraph and dynet
• Exploring the space of graphs with fixed discrete curvatures
• A machine learning pipeline for discriminant pathways identification
• Evaluating sources of variability in pathway profiling