  Learning Continuous-Time Dynamic Graphs (C-TDGs) requires accurately modeling
spatio-temporal information on streams of irregularly sampled events. While
many methods have been proposed recently, we find that most message passing-,
recurrent- or self-attention-based methods perform poorly on long-range tasks.
These tasks require correlating information that occurred "far" away from the
current event, either spatially (higher-order node information) or along the
time dimension (events occurred in the past). To address long-range
dependencies, we introduce Continuous-Time Graph Anti-Symmetric Network (CTAN).
Grounded within the ordinary differential equations framework, our method is
designed for efficient propagation of information. In this paper, we show how
CTAN's (i) long-range modeling capabilities are substantiated by theoretical
findings and how (ii) its empirical performance on synthetic long-range
benchmarks and real-world benchmarks is superior to other methods. Our results
motivate CTAN's ability to propagate long-range information in C-TDGs as well
as the inclusion of long-range tasks as part of temporal graph models
evaluation.
