Temporal Neighbourhood Aggregation：通过递归变分图卷积预测时态图中的未来链接
Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions
摘要：
图形已经成为一种重要的方式来表示跨各种科学学科的大型，复杂且通常是时态的数据集。然而，当图被用作机器学习模型的输入时，这种丰富的时间信息在学习过程中经常被忽视，导致在某些时间推理任务上的次优性能。为了解决这个问题，我们引入了时间邻域聚合（TNA），一种新的顶点表示模型架构，旨在捕获拓扑和时间信息，直接预测未来的图形状态。我们的模型利用图中不同深度的分层递归来探索时间邻域的变化，同时不需要额外的特征或标签。最终的顶点表示是使用变分采样创建的，并经过优化以直接预测序列中的下一个图。通过对真实和合成基准数据集进行广泛的实验评估，我们的主张得到了加强，与竞争方法相比，我们的方法表现出更优越的性能，在真实世界数据集上预测新的时间边缘时，性能超过它们多达23%，同时还需要更少的整体模型参数。
英文摘要：
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 infernce 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 reinforced by extensive experimental evaluation on both real and synthetic benchmark datasets, where our approach demonstrates superior performance compared to competing methods, out-performing them at predicting new temporal edges by as much as 23% on real-world datasets, whilst also requiring fewer overall model parameters.
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