Physically-interpretable classification of biological network dynamics for complex collective motions
This work addresses the problem of interpreting biological network dynamics for researchers in fields like biology and engineering, though it is incremental as it builds on existing spectral analysis methods.
The paper tackled the classification of complex collective motion networks using physically-interpretable dynamical properties, applying graph dynamic mode decomposition to a ballgame example to classify strategic motions and identify critical contextual node information and label-specific spectra.
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.