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Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions

Bonner, Stephen and Atapour-Abarghouei, Amir and Jackson, Phillip and Brennan, John and Kureshi, Ibad and Theodoropoulos, Georgios and McGough, Stephen and Obara, Boguslaw (2019) 'Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions.', in 2019 IEEE International Conference on Big Data (Big Data). Piscataway, NJ: IEEE, pp. 5336-5345.

Abstract

Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide range of 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.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1109/BigData47090.2019.9005545
Publisher statement:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:No date available
Date deposited:27 November 2019
Date of first online publication:23 February 2020
Date first made open access:19 March 2020

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