Multicoated and Folded Graph Neural Networks with Strong Lottery Tickets

Jiale Yan, Hiroaki Ito, Ángel López García-Arias, Yasuyuki Okoshi, Hikari Otsuka, Kazushi Kawamura, Thiem Van Chu, Masato Motomura

Keywords: Graph neural networks, Lottery ticket hypothesis, Recurrent neural networks, Pruning

TL;DR: This paper adopts multicoated supermasks and the folding method to achieve high sparsity, competitive performance, and high memory efficiency for GNNs without weight training.

Abstract: The Strong Lottery Ticket Hypothesis (SLTH) demonstrates the existence of high-performing subnetworks within a randomly initialized model, discoverable through pruning a convolutional neural network (CNN) without any weight training. A recent study, called Untrained GNNs Tickets (UGT), expanded SLTH from CNNs to shallow graph neural networks (GNNs). However, discrepancies persist when comparing baseline models with learned dense weights. Additionally, there remains an unexplored area in applying SLTH to deeper GNNs, which, despite delivering improved accuracy with additional layers, suffer from excessive memory requirements. To address these challenges, this work utilizes Multicoated Supermasks (M-Sup), a scalar pruning mask method, and implements it in GNNs by proposing a strategy for setting its pruning thresholds adaptively. In the context of deep GNNs, this research uncovers the existence of untrained recurrent networks, which exhibit performance on par with their trained feed-forward counterparts. This paper also introduces the Multi-Stage Folding and Unshared Masks methods to expand the search space in terms of both architecture and parameters. Through the evaluation of various datasets, including the Open Graph Benchmark (OGB), this work establishes a triple-win scenario for SLTH-based GNNs: by achieving high sparsity, competitive performance, and high memory efficiency with up to 98.7\% reduction, it demonstrates suitability for energy-efficient graph processing.

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Submission Number: 44