  This paper proposes fractional order graph neural networks (FGNNs), optimized
by the approximation strategy to address the challenges of local optimum of
classic and fractional graph neural networks which are specialised at
aggregating information from the feature and adjacent matrices of connected
nodes and their neighbours to solve learning tasks on non-Euclidean data such
as graphs. Meanwhile the approximate calculation of fractional order gradients
also overcomes the high computational complexity of fractional order
derivations. We further prove that such an approximation is feasible and the
FGNN is unbiased towards global optimization solution. Extensive experiments on
citation networks show that FGNN achieves great advantage over baseline models
when selected appropriate fractional order.
