Physically-interpretable classification of biological network dynamics for complex collective motions

Abstract

Understanding biological network dynamics is a fundamental issue in various scientific and engineering fields. Network theory is capable of revealing the relationship between elements and their propagation; however, for complex collective motions, the network properties often transiently and complexly change. A fundamental question addressed here pertains to the classification of collective motion network based on physically-interpretable dynamical properties. Here we apply a data-driven spectral analysis called graph dynamic mode decomposition, which obtains the dynamical properties for collective motion classification. Using a ballgame as an example, we classified the strategic collective motions in different global behaviours and discovered that, in addition to the physical properties, the contextual node information was critical for classification. Furthermore, we discovered the label-specific stronger spectra in the relationship among the nearest agents, providing physical and semantic interpretations. Our approach contributes to the understanding of principles of biological complex network dynamics from the perspective of nonlinear dynamical systems.

42 pages with 7 figures and 3 tables. The latest version is published in Scientific Reports, 2020

References in corpus (6)

• Reaction-diffusion processes and metapopulation models in heterogeneous networks
• Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
• Structure and Time-Evolution of an Internet Dating Community
• Revealing physical interaction networks from statistics of collective dynamics
• Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
• Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables

Cited by in corpus (4)

• Data-driven Analysis for Understanding Team Sports Behaviors
• Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study
• CKNet: A Convolutional Neural Network Based on Koopman Operator for Modeling Latent Dynamics from Pixels
• DeepGraphDMD: Interpretable Spatio-Temporal Decomposition of Non-linear Functional Brain Network Dynamics