  Graph Neural Networks (GNNs) have become increasingly important in recent
years due to their state-of-the-art performance on many important downstream
applications. Existing GNNs have mostly focused on learning a single node
representation, despite that a node often exhibits polysemous behavior in
different contexts. In this work, we develop a persona-based graph neural
network framework called PersonaSAGE that learns multiple persona-based
embeddings for each node in the graph. Such disentangled representations are
more interpretable and useful than a single embedding. Furthermore, PersonaSAGE
learns the appropriate set of persona embeddings for each node in the graph,
and every node can have a different number of assigned persona embeddings. The
framework is flexible enough and the general design helps in the wide
applicability of the learned embeddings to suit the domain. We utilize publicly
available benchmark datasets to evaluate our approach and against a variety of
baselines. The experiments demonstrate the effectiveness of PersonaSAGE for a
variety of important tasks including link prediction where we achieve an
average gain of 15% while remaining competitive for node classification.
Finally, we also demonstrate the utility of PersonaSAGE with a case study for
personalized recommendation of different entity types in a data management
platform.
