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

Bonner, Stephen; Atapour-Abarghouei, Amir; Jackson, Phillip; Brennan, John; Kureshi, Ibad; Theodoropoulos, Georgios; McGough, Stephen; Obara, Boguslaw

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.

Citation

Bonner, S., Atapour-Abarghouei, A., Jackson, P., Brennan, J., Kureshi, I., Theodoropoulos, G., McGough, S., & Obara, B. (2019, December). Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions. Presented at IEEE International Conference on Big Data (Deep Graph Learning: Methodologies and Applications), Los Angeles, CA, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: IEEE International Conference on Big Data (Deep Graph Learning: Methodologies and Applications)
• Start Date: Dec 9, 2019
• End Date: Dec 12, 2019
• Online Publication Date: Feb 23, 2020
• Publication Date: 2019
• Deposit Date: Nov 5, 2019
• Publicly Available Date: Mar 19, 2020
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 5336-5345
• Book Title: 2019 IEEE International Conference on Big Data (Big Data).
• DOI: https://doi.org/10.1109/bigdata47090.2019.9005545
• Public URL: https://durham-repository.worktribe.com/output/1141030