Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND

arXiv:2404.17099v1 Announce Type: new
Abstract: We introduce the FRactional-Order graph Neural Dynamical network (FROND), a new continuous graph neural network (GNN) framework. Unlike traditional continuous GNNs that rely on integer-order differential equations, FROND employs the Caputo fractional derivative to leverage the non-local properties of fractional calculus. This approach enables the capture of long-term dependencies in feature updates, moving beyond the Markovian update mechanisms in conventional integer-order models and offering enhanced capabilities in graph representation learning. We offer an interpretation of …

arxiv calculus cs.lg cs.ne graph graph neural networks networks neural networks type