A Biased Graph Neural Network Sampler with Near-Optimal Regret

Authors

Qingru Zhang, Georgia Institute of Technology, USA & Amazon Shanghai AI Lab, China &Follow
David Wipf, Amazon Shanghai AI Lab, China
Quan Gan, Amazon Shanghai AI Lab, China
Le Song, Georgia Institute of Technology, USA & Mohamed bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

arXiv

Abstract

Graph neural networks (GNN) have recently emerged as a vehicle for applying deep network architectures to graph and relational data. However, given the increasing size of industrial datasets, in many practical situations the message passing computations required for sharing information across GNN layers are no longer scalable. Although various sampling methods have been introduced to approximate full-graph training within a tractable budget, there remain unresolved complications such as high variances and limited theoretical guarantees. To address these issues, we build upon existing work and treat GNN neighbor sampling as a multi-armed bandit problem but with a newly-designed reward function that introduces some degree of bias designed to reduce variance and avoid unstable, possibly-unbounded pay outs. And unlike prior bandit-GNN use cases, the resulting policy leads to near-optimal regret while accounting for the GNN training dynamics introduced by SGD. From a practical standpoint, this translates into lower variance estimates and competitive or superior test accuracy across several benchmarks. Copyright © 2021, The Authors. All rights reserved.

DOI

10.48550/arXiv.2103.01089

Publication Date

11-14-2021

Comments

Preprints: arXiv

Recommended Citation

Q. Zhang, D. Wipf, Q. Gan, and L. Song, "A biased graph neural network sampler with near-optimal regret," 2021, arXiv:2103.01089