时间图偏移重构：面向时间鲁棒图表示学习
Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning
摘要：
图是用于表示复杂高维数据集中元素之间关系的常用构造。许多现实世界的现象本质上是动态的，这意味着用于表示它们的任何图形都是固有的时间。然而，许多被设计为捕获关于这些图的结构的知识的机器学习模型在创建图的表示时忽略了这种丰富的时间信息。这导致模型在用于预测图的未来状态时表现不佳--尤其是当时间戳之间的增量不小时。在这项工作中，我们探索了一种新的训练过程和相关的无监督模型，该模型创建了优化的图形表示，以预测图形的未来状态。我们利用图卷积神经网络将图编码为潜在表示，然后我们使用它来训练我们的时间偏移重建方法，该方法受到自动编码器的启发，以预测稍后的时间点-未来的多个时间步长。使用我们的方法，我们表现出优越的性能为未来的链接预测的任务相比，非时间的最先进的基线。我们证明了我们的方法能够在现实世界的数据集上超过非时间基线38%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
