  Representation learning on temporal interaction graphs (TIG) is to model
complex networks with the dynamic evolution of interactions arising in a broad
spectrum of problems. Existing dynamic embedding methods on TIG discretely
update node embeddings merely when an interaction occurs. They fail to capture
the continuous dynamic evolution of embedding trajectories of nodes. In this
paper, we propose a two-module framework named ConTIG, a continuous
representation method that captures the continuous dynamic evolution of node
embedding trajectories. With two essential modules, our model exploit
three-fold factors in dynamic networks which include latest interaction,
neighbor features and inherent characteristics. In the first update module, we
employ a continuous inference block to learn the nodes' state trajectories by
learning from time-adjacent interaction patterns between node pairs using
ordinary differential equations. In the second transform module, we introduce a
self-attention mechanism to predict future node embeddings by aggregating
historical temporal interaction information. Experiments results demonstrate
the superiority of ConTIG on temporal link prediction, temporal node
recommendation and dynamic node classification tasks compared with a range of
state-of-the-art baselines, especially for long-interval interactions
prediction.
