Research in Adaptive Information Flow in Graph Neural Networks

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.

AIMS's impact will be felt across domains of acute need within the UK. We expect AIMS to benefit: UK economic performance, through start-up creation; existing UK firms, both through research and addressing skills needs; UK health, by contributing to cancer research, and quality of life, through the delivery of autonomous vehicles; UK public understanding of and policy related to the transformational societal change engendered by autonomous systems.

Autonomous systems are acknowledged by essentially all stakeholders as important to the future UK economy. PwC claim that there is a £232 billion opportunity offered by AI to the UK economy by 2030 (10% of GDP). AIMS has an excellent track record of leadership in spinout creation, and will continue to foster the commercial projects of its students, through the provision of training in IP, licensing and entrepreneurship. With the help of Oxford Science Innovation (investment fund) and Oxford University Innovation (technology transfer office), student projects will be evaluated for commercial potential.

AIMS will also concretely contribute to UK economic competitiveness by meeting the UK's needs for experts in autonomous systems. To meet this need, AIMS will train cohorts with advanced skills that span the breadth of AI, machine learning, robotics, verification and sensor systems. The relevance of the training to the needs of industry will be ensured by the industrial partnerships at the heart of AIMS. These partnerships will also ensure that AIMS will produce research that directly targets UK industrial needs. Our partners span a wide range of UK sectors, including energy, transport, infrastructure, factory automation, finance, health, space and other extreme environments.

The autonomous systems that AIMS will enable also offer the prospect of epochal change in the UK's quality of life and health. As put by former Digital Secretary Matt Hancock, "whether it's improving travel, making banking easier or helping people live longer, AI is already revolutionising our economy and our society." AIMS will help to realise this potential through its delivery of trained experts and targeted research. In particular, two of the four Grand Challenge missions in the UK Industrial Strategy highlight the positive societal impact underpinned by autonomous systems. The "Artificial Intelligence and data" challenge has as its mission to "Use data, Artificial Intelligence and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030". To this mission, AIMS will contribute the outputs of its research pillar on cancer research. The "Future of mobility" challenge highlights the importance the autonomous vehicles will have in making transport "safer, cleaner and better connected." To this challenge, AIMS offers the world-leading research of its robotic systems research pillar.

AIMS will further promote the positive realisation of autonomous technologies through direct influence on policy. The world-leading academics amongst AIMS's supervisory pool are well-connected to policy formation e.g. Prof Osborne serving as a Commissioner on the Independent Commission on the Future of Work. Further, Dr Dan Mawson, Head of the Economy Unit; Economy and Strategic Analysis Team at BEIS will serve as an advisor to AIMS, ensuring bidirectional influence between policy objectives and AIMS research and training.

Broad understanding of autonomous systems is crucial in making a society robust to the transformations they will engender. AIMS will foster such understanding through its provision of opportunities for AIMS students to directly engage with the public. Given the broad societal importance of getting autonomous systems right, AIMS will deliver core training on the ethical, governance, economic and societal implications of autonomous systems.