An Algorithm for Automatically Discovering Dynamical Rules of Adaptive Network Evolution from Empirical Data

An algorithm is proposed for automatic discovery of a set of dynamical rules that best captures both state transition and topological transformation in the empirical data showing time evolution of adaptive networks. Graph rewriting systems are used as the basic model framework to represent state transition and topological transformation simultaneously. Network evolution is formulated in two phases: extraction and replacement of subnetworks. For each phase, multiple methods of rule discovery are proposed and will be explored. This paper reports the basic architecture of the algorithm, as well as its implementation and evaluation plan.