  The task of inductive link prediction in (discrete) attributed multigraphs
infers missing attributed links (relations) between nodes in new test
multigraphs. Traditional relational learning methods face the challenge of
limited generalization to test multigraphs containing both novel nodes and
novel relation types not seen in training. Recently, under the only assumption
that all relation types share the same structural predictive patterns (single
task), Gao et al. (2023) proposed a link prediction method using the
theoretical concept of double equivariance (equivariance for nodes & relation
types), in contrast to the (single) equivariance (only for nodes) used to
design Graph Neural Networks (GNNs). In this work we further extend the double
equivariance concept to multi-task double equivariance, where we define link
prediction in attributed multigraphs that can have distinct and potentially
conflicting predictive patterns for different sets of relation types (multiple
tasks). Our empirical results on real-world datasets demonstrate that our
approach can effectively generalize to test graphs with multi-task structures
without access to additional information.
