Leveraging Temporal Graph Networks Using Module Decoupling
Or Feldman Chaim Baskin
Modern approaches for learning on dynamic graphs have adopted the use of
batches instead of applying updates one by one. The use of batches allows these
techniques to become helpful in streaming scenarios where updates to graphs are
received at extreme speeds. Using batches, however, forces the models to update
infrequently, which results in the degradation of their performance. In this
work, we suggest a decoupling strategy that enables the models to update
frequently while using batches. By decoupling the core modules of temporal
graph networks and implementing them using a minimal number of learnable
parameters, we have developed the Lightweight Decoupled Temporal Graph Network
(LDTGN), an exceptionally efficient model for learning on dynamic graphs. LDTG
was validated on various dynamic graph benchmarks, providing comparable or
state-of-the-art results with significantly higher throughput than previous
art. Notably, our method outperforms previous approaches by more than 20\% on
benchmarks that require rapid model update rates, such as USLegis or UNTrade.
The code to reproduce our experiments is available at
\href{https://orfeld415.github.io/module-decoupling}{this http url}.