PersonaSAGE: A Multi-Persona Graph Neural Network

TL;DR

PersonaSAGE introduces a multi-persona graph neural network that learns multiple, interpretable embeddings per node, improving performance on link prediction and enabling personalized recommendations across various domains.

Contribution

It proposes a novel framework for learning multiple persona-based node embeddings, enhancing interpretability and applicability in diverse graph-based tasks.

Findings

Achieves an average of 15% gain in link prediction performance.

Remains competitive in node classification tasks.

Demonstrates utility in personalized recommendations.

Abstract

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.