Self-Evolving Graph Neural Network Design Inspired by Neural Cellular Automata

Abstract

Designing effective graph neural networks for node classification still depends heavily on human intuition, repeated trial-and-error, and expert knowledge of architectural components. To reduce this manual burden, we introduce a self-evolving neural architecture search framework inspired by Neural Cellular Automata (NCA), in which a population of NCA-based controllers autonomously generates and improves candidate GNN architectures over successive generations. Each controller encodes layer-wise architectural decisions, including layer activation, message-passing operator type, and hidden dimensionality, thereby defining a dynamic search space of graph models. The sampled architectures are trained and evaluated on node classification tasks, while their quality is assessed through a fitness function that jointly considers validation accuracy, validation loss, architectural depth, and parameter count relative to a baseline model. In this way, the proposed framework promotes not only predictive performance but also structural efficiency. Evolution proceeds through elitist retention, tournament selection, and mutation of controller parameters, enabling progressive adaptation of the architecture distribution without hand-crafted design rules.