  Recent research has highlighted the role of relational inductive biases in
building learning agents that can generalize and reason in a compositional
manner. However, while relational learning algorithms such as graph neural
networks (GNNs) show promise, we do not understand how effectively these
approaches can adapt to new tasks. In this work, we study the task of logical
generalization using GNNs by designing a benchmark suite grounded in
first-order logic. Our benchmark suite, GraphLog, requires that learning
algorithms perform rule induction in different synthetic logics, represented as
knowledge graphs. GraphLog consists of relation prediction tasks on 57 distinct
logical domains. We use GraphLog to evaluate GNNs in three different setups:
single-task supervised learning, multi-task pretraining, and continual
learning. Unlike previous benchmarks, our approach allows us to precisely
control the logical relationship between the different tasks. We find that the
ability for models to generalize and adapt is strongly determined by the
diversity of the logical rules they encounter during training, and our results
highlight new challenges for the design of GNN models. We publicly release the
dataset and code used to generate and interact with the dataset at
https://www.cs.mcgill.ca/~ksinha4/graphlog.
