  Temporal Graph Neural Networks have garnered substantial attention for their
capacity to model evolving structural and temporal patterns while exhibiting
impressive performance. However, it is known that these architectures are
encumbered by issues that constrain their performance, such as over-squashing
and over-smoothing. Meanwhile, Transformers have demonstrated exceptional
computational capacity to effectively address challenges related to long-range
dependencies. Consequently, we introduce Todyformer-a novel Transformer-based
neural network tailored for dynamic graphs. It unifies the local encoding
capacity of Message-Passing Neural Networks (MPNNs) with the global encoding of
Transformers through i) a novel patchifying paradigm for dynamic graphs to
improve over-squashing, ii) a structure-aware parametric tokenization strategy
leveraging MPNNs, iii) a Transformer with temporal positional-encoding to
capture long-range dependencies, and iv) an encoding architecture that
alternates between local and global contextualization, mitigating
over-smoothing in MPNNs. Experimental evaluations on public benchmark datasets
demonstrate that Todyformer consistently outperforms the state-of-the-art
methods for downstream tasks. Furthermore, we illustrate the underlying aspects
of the proposed model in effectively capturing extensive temporal dependencies
in dynamic graphs.
