  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.
