Title: Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning,
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 the future state
of the graph -- especially when the delta between time stamps 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 -- multiple time steps into the future. Using our method, we
demonstrate superior performance for the task of future link prediction
compared with none-temporal state-of-the-art baselines. We show our approach to
be capable of outperforming non-temporal baselines by 38% on a real world
dataset.