Graph Lottery Ticket Automated

Published: 16 Jan 2024, Last Modified: 12 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: Lottery Ticket Hypothesis, Graph Neural Networks, Graph Neural Tangent Kernel
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Abstract: Graph Neural Networks (GNNs) have emerged as the leading deep learning models for graph-based representation learning. However, the training and inference of GNNs on large graphs remain resource-intensive, impeding their utility in real-world scenarios and curtailing their applicability in deeper and more sophisticated GNN architectures. To address this issue, the Graph Lottery Ticket (GLT) hypothesis assumes that GNN with random initialization harbors a pair of core subgraph and sparse subnetwork, which can yield comparable performance and higher efficiency to that of the original dense network and complete graph. Despite that GLT offers a new paradigm for GNN training and inference, existing GLT algorithms heavily rely on trial-and-error pruning rate tuning and scheduling, and adhere to an irreversible pruning paradigm that lacks elasticity. Worse still, current methods suffer scalability issues when applied to deep GNNs, as they maintain the same topology structure across all layers. These challenges hinder the integration of GLT into deeper and larger-scale GNN contexts. To bridge this critical gap, this paper introduces an $\textbf{A}$daptive, $\textbf{D}$ynamic, and $\textbf{A}$utomated framework for identifying $\textbf{G}$raph $\textbf{L}$ottery $\textbf{T}$ickets ($\textbf{AdaGLT}$). Our proposed method derives its key advantages and addresses the above limitations through the following three aspects: 1) tailoring layer-adaptive sparse structures for various datasets and GNNs, thus endowing it with the capability to facilitate deeper GNNs; 2) integrating the pruning and training processes, thereby achieving a dynamic workflow encompassing both pruning and restoration; 3) automatically capturing graph lottery tickets across diverse sparsity levels, obviating the necessity for extensive pruning parameter tuning. More importantly, we rigorously provide theoretical proofs to guarantee $\textbf{AdaGLT}$ to mitigate over-smoothing issues and obtain improved sparse structures in deep GNN scenarios. Extensive experiments demonstrate that $\textbf{AdaGLT}$ outperforms state-of-the-art competitors across multiple graph datasets of various scales and types, particularly in scenarios involving deep GNNs.
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Primary Area: learning on graphs and other geometries & topologies
Submission Number: 620