CTRL：时序异质信息网络上的连续时间表示学习
CTRL: Continuous-Time Representation Learning on Temporal Heterogeneous Information Network
AI总结：
针对现有时序异构图表示学习方法归纳能力不足、忽略高阶拓扑演化的问题，提出整合异质注意力、边基霍克斯过程和动态中心性的CTRL模型，通过未来子图预测训练，在三个基准数据集上性能优于现有最优方法。
摘要：
时序异构图上的归纳式表示学习对异质信息网络（HIN）的可扩展深度学习至关重要，这类网络是随时间变化的，例如引文网络。然而，现有大多数方法不具备归纳能力，因此无法处理新节点或新边。此外，以往的时序图嵌入方法通常通过时序链路预测任务进行训练，以模拟时序图的链路形成过程，却忽略了时序图上高阶拓扑结构的演化。为填补这些空白，我们提出了一种应用于时序HIN的连续时间表示学习（CTRL）模型。为保留异质节点特征与时序结构，CTRL在单层中整合了三个组件：1）用于衡量节点间语义相关性的\textbf{异质注意力}单元；2）用于捕获异质节点间时序影响的\textbf{基于边的霍克斯过程（edge-based Hawkes process）}；3）用于表征节点动态重要性的\textbf{动态中心性（dynamic centrality）}。我们通过未来事件（子图）预测任务训练CTRL模型，以捕获高阶网络结构的演化。我们在三个基准数据集上开展了大量实验，结果表明该模型显著提升了性能，优于多种当前最优方法。我们还进行了消融研究，以验证模型设计的有效性。
英文摘要：
Inductive representation learning on temporal heterogeneous graphs is crucial for scalable deep learning on heterogeneous information networks (HINs) which are time-varying, such as citation networks. However, most existing approaches are not inductive and thus cannot handle new nodes or edges. Moreover, previous temporal graph embedding methods are often trained with the temporal link prediction task to simulate the link formation process of temporal graphs, while ignoring the evolution of high-order topological structures on temporal graphs. To fill these gaps, we propose a Continuous-Time Representation Learning (CTRL) model on temporal HINs. To preserve heterogeneous node features and temporal structures, CTRL integrates three parts in a single layer, they are 1) a \emph{heterogeneous attention} unit that measures the semantic correlation between nodes, 2) a \emph{edge-based Hawkes process} to capture temporal influence between heterogeneous nodes, and 3) \emph{dynamic centrality} that indicates the dynamic importance of a node. We train the CTRL model with a future event (a subgraph) prediction task to capture the evolution of the high-order network structure. Extensive experiments have been conducted on three benchmark datasets. The results demonstrate that our model significantly boosts performance and outperforms various state-of-the-art approaches. Ablation studies are conducted to demonstrate the effectiveness of the model design.
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