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Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions

Lookup NU author(s): Stephen BonnerORCiD, Dr Amir Atapour AbarghoueiORCiD, Phillip Jackson, Dr John Brennan, Dr Stephen McGough, Professor Boguslaw ObaraORCiD



This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE, 2019.

For re-use rights please refer to the publisher's terms and conditions.


Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide rangeof scientific disciplines. However, when graphs are used as input to machine learning models, this rich temporal information is frequently disregarded during the learning process, resulting in suboptimal performance on certain temporal inference tasks. To combat this, we introduce Temporal Neighbourhood Aggregation (TNA), a novel vertex representation model architecture designed to capture both topological and temporal information to directly predict future graph states. Our model exploits hierarchical recurrence at different depths within the graph to enable exploration of changes in temporal neighbourhoods, whilst requiring no additional features or labels to be present. The final vertex representations are created using variational sampling and are optimised to directly predict the next graph in the sequence. Our claims are supported by experimental evaluation on both real and synthetic benchmark datasets, where our approach demonstrates superior performance compared to competing methods, outperforming them at predicting new temporal edges by as much as 23% on real-world datasets, whilst also requiring fewer overall model parameters.

Publication metadata

Author(s): Bonner S, Atapour Abarghouei A, Jackson P, Brennan J, Kureshi I, Theodoropoulos G, McGough S, Obara B

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: IEEE International Conference on Big Data (Big Data 2019)

Year of Conference: 2019

Pages: 5336-5345

Online publication date: 24/02/2020

Acceptance date: 27/11/2019

Date deposited: 24/02/2020

Publisher: IEEE


DOI: 10.1109/BigData47090.2019.9005545

Library holdings: Search Newcastle University Library for this item

ISBN: 9781728108582