Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning

Bonner, Stephen; Brennan, John; Kureshi, Ibad; Theodoropoulos, Georgios; McGough, Stephen; Obara, Boguslaw

Abstract

Graphs are a commonly used construct for representing relationships between elements in complex high dimensional datasets. Many real-world phenomenon are dynamic in nature, meaning that any graph used to represent them is inherently temporal. However, many of the machine learning models designed to capture knowledge about the structure of these graphs ignore this rich temporal information when creating representations of the graph. This results in models which do not perform well when used to make predictions about a later point in a graph’s time series when the delta between time steps is not small. In this work, we explore a novel training procedure and an associated unsupervised model which creates graph representations optimised to predict the future state of the graph. We make use of graph convolutional neural networks to encode the graph into a latent representation, which we then use to train our temporal offset reconstruction method, inspired by auto-encoders, to predict a later time point. Using our method, we demonstrate superior performance for the task of future link prediction compared with none-temporal stateof-the-art baselines. We show our approach to be capable of outperforming non-temporal baselines by 38% on a real world dataset.

Citation

Bonner, S., Brennan, J., Kureshi, I., Theodoropoulos, G., McGough, S., & Obara, B. (2018, December). Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning. Presented at IEEE International Conference on Big Data., Seattle, WA, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: IEEE International Conference on Big Data.
• Start Date: Dec 10, 2018
• End Date: Dec 13, 2018
• Acceptance Date: Nov 10, 2018
• Publication Date: Dec 13, 2018
• Deposit Date: Nov 10, 2018
• Publisher: Institute of Electrical and Electronics Engineers
• Public URL: https://durham-repository.worktribe.com/output/1145078
• Publisher URL: http://cci.drexel.edu/bigdata/bigdata2018/index.html