  Graph neural networks (GNNs) are a powerful inductive bias for modelling
algorithmic reasoning procedures and data structures. Their prowess was mainly
demonstrated on tasks featuring Markovian dynamics, where querying any
associated data structure depends only on its latest state. For many tasks of
interest, however, it may be highly beneficial to support efficient data
structure queries dependent on previous states. This requires tracking the data
structure's evolution through time, placing significant pressure on the GNN's
latent representations. We introduce Persistent Message Passing (PMP), a
mechanism which endows GNNs with capability of querying past state by
explicitly persisting it: rather than overwriting node representations, it
creates new nodes whenever required. PMP generalises out-of-distribution to
more than 2x larger test inputs on dynamic temporal range queries,
significantly outperforming GNNs which overwrite states.

Machine Learning;Artificial Intelligence;Social and Information Networks;Machine Learning