Graph-Coupled Oscillator Networks

These models have recently been successfully applied in a variety of tasks such as computer vision and graphics Monti et al. (2017), recommender systems Ying et al. (2018), transportation Derrow-Pinion et al. (2021), computational chemistry (Gilmer et al., 2017), drug discovery Gaudelet et al. (2021), physics (Shlomi et al., 2020), and analysis of social networks (see Zhou et al. (2019); Bronstein et al. (2021) for additional applications). Several recent works proposed Graph ML models based on differential equations coming from physics Avelar et al. (2019); Poli et al. (2019b); Zhuang et al. (2020); Xhonneux et al. (2020b), including diffusion Chamberlain et al. (2021b) and wave Eliasof et al. (2021) equations and geometric equations such as Beltrami Chamberlain et al. (2021a) and Ricci Topping et al. (2021) flows. Such approaches allow not only to recover popular GNN models as discretization schemes for the underling differential equations, but also, in some cases, can address problems encountered in traditional GNNs such as oversmoothing Nt & Maehara (2019); Oono & Suzuki (2020) and bottlenecks Alon & Yahav (2021). In this paper, we propose a novel physically-inspired approach to learning on graphs. Our framework, termed GraphCON (Graph-Coupled Oscillator Network) builds upon suitable time-discretizations of a specific class of ordinary differential equations (ODEs) that model the dyanmics of a network of non-linear forced and damped oscillators, which are coupled via the adjacency structure of the underlying graph. Graph-coupled oscillators are often encountered in mechanical, electronic, and biological systems, and have been studied extensively Strogatz (2015), with a prominent example being functional circuits in the brain such as cortical columns Stiefel & Ermentrout (2016).