Repetition Makes Perfect: Recurrent Sum-GNNs Match Message Passing Limit

Abstract: We provide first tight bounds for the expressivity of Recurrent Graph Neural Networks (recurrent GNNs) with finite-precision parameters. We prove that recurrent GNNs, with sum aggregation and ReLU activation, can emulate any graph algorithm that respects the natural message-passing invariance induced by the color refinement (or Weisfeiler-Leman) algorithm. While it is well known that the expressive power of GNNs is limited by this invariance [Morris et al., AAAI 2019; Xu et al., ICLR 2019], we establish that recurrent GNNs can actually reach this limit. This is in contrast to non-recurrent GNNs, which have the power of Weisfeiler-Leman only in a very weak, "non-uniform", sense where every graph size requires a different GNN model to compute with. The emulation we construct introduces only a polynomial overhead in both time and space. Furthermore, we show that by incorporating random initialization, recurrent GNNs can emulate all graph algorithms, implying in particular that any graph algorithm with polynomial-time complexity can be emulated by a recurrent GNN with random initialization, running in polynomial time.