  Learning effective representations for Continuous-Time Dynamic Graphs (CTDGs)
has garnered significant research interest, largely due to its powerful
capabilities in modeling complex interactions between nodes. A fundamental and
crucial requirement for representation learning in CTDGs is the appropriate
estimation and preservation of proximity. However, due to the sparse and
evolving characteristics of CTDGs, the spatial-temporal properties inherent in
high-order proximity remain largely unexplored. Despite its importance, this
property presents significant challenges due to the computationally intensive
nature of personalized interaction intensity estimation and the dynamic
attributes of CTDGs. To this end, we propose a novel Correlated
Spatial-Temporal Positional encoding that incorporates a parameter-free
personalized interaction intensity estimation under the weak assumption of the
Poisson Point Process. Building on this, we introduce the Dynamic Graph
Transformer with \Correlated Spatial-Temporal Positional Encoding (CorDGT),
which efficiently retains the evolving spatial-temporal high-order proximity
for effective node representation learning in CTDGs. Extensive experiments on
seven small and two large-scale datasets demonstrate the superior performance
and scalability of the proposed CorDGT.
