Research in Zero-Shot Coordination and Delay Graph Neural Networks

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This research follows on from the second mini-project undertaken in the first year of the AIMS CDT, in the area of graph machine learning (ML).Graph neural networks have enjoyed tremendous popularity in recent years. Graph-structured data provides additional structural and relational information beyond tabular data, allowing for geometric interpretation of data domains and the principled incorporation of inductive biases (P. W. Battaglia et al. 2018; Bronstein et al. 2021; J. Zhou et al. 2020). The dominant paradigm in graph neural networks, message passing (Gilmer et al. 2017), permits only local node interactions in the classical case, and subsequently suffers from issues such as over-smoothing and over-squashing which reduce performance (Di Giovanni, Giusti, et al. 2023; Nt and Maehara 2019; Oono and Suzuki 2019; Topping et al. 2021). Methods to address such issues and improve on classical message passing, such as graph rewiring (Gasteiger et al. 2019; Topping et al. 2021), multi-hop message-passing (Abboud et al. 2022; Abu-El-Haija, Perozzi, et al. 2019) and graph Transformers (Dwivedi and Bresson 2020; Rampasek et al. 2022; Vaswani et al. 2017), but often they dilute or throw away the inductive bias provided by topology, rather than incorporating it into the message passing process. To make better use of this inductive bias, we may want to use it to determine not only whether and how two nodes in a graph interact, but also when. This research has so far resulted in a conference paper, DRew (Gutteridge et al. 2023), which was accepted at ICML 2023. It is the first work to consider such adaptive information flow in graph neural networks, and in ongoing and future projects I hope to continue to investigate this framework, for static graphs and long-range interactions, but also for applications such as temporal graphs, point clouds and protein design.This proposal consists of machine learning research, which falls under the EPSRC research areas of engineering and information technologies. There is no explicit industry collaboration, but the research has potential applications in areas such as computational chemistry.