Self-Organized Multi-Scale Event Graph Learning

Abstract

This paper proposes a novel approach to understanding and predicting complex system dynamics by leveraging the principles of self-organization and multi-scale event propagation. The core idea is to construct a self-organized event graph that mimics the behavior of biological neural networks, specifically focusing on the propagation of events across multiple scales. This is achieved through the design of a hierarchical network structure where different layers process event information at varying scales. An adaptive algorithm then dynamically adjusts node connections and event propagation pathways within the graph based on the observed event dynamics. The resulting event graph provides a powerful tool for capturing the intricate patterns and dependencies inherent in complex systems. Mathematical formulation focuses on event propagation, graph structure evolution, and the adaptive learning process. Key elements include event representation, graph adjacency matrix, propagation rules, and the objective function guiding network adaptation. The system is designed to learn patterns of complex behavior from data, offering a new perspective on modeling dynamic systems. ---