  The field of temporal graph learning aims to learn from evolving network data
to forecast future interactions. Given a collection of observed temporal
graphs, is it possible to predict the evolution of an unseen network from the
same domain? To answer this question, we first present the Temporal Graph
Scaling (TGS) dataset, a large collection of temporal graphs consisting of
eighty-four ERC20 token transaction networks collected from 2017 to 2023. Next,
we evaluate the transferability of Temporal Graph Neural Networks (TGNNs) for
the temporal graph property prediction task by pre-training on a collection of
up to sixty-four token transaction networks and then evaluating the downstream
performance on twenty unseen token networks. We find that the neural scaling
law observed in NLP and Computer Vision also applies in temporal graph
learning, where pre-training on greater number of networks leads to improved
downstream performance. To the best of our knowledge, this is the first
empirical demonstration of the transferability of temporal graphs learning. On
downstream token networks, the largest pre-trained model outperforms single
model TGNNs on thirteen unseen test networks. Therefore, we believe that this
is a promising first step towards building foundation models for temporal
graphs.
