Graph Sparsification via Mixture of Graphs

TL;DR

This paper introduces Mixture-of-Graphs (MoG), a novel graph sparsification method that dynamically tailors edge pruning for each node, leading to more efficient GNNs with maintained or improved performance on large datasets.

Contribution

MoG leverages multiple sparsifier experts and a mixture approach on the Grassmann manifold to produce customized, high-sparsity subgraphs for each node, enhancing efficiency and accuracy.

Findings

Achieves higher sparsity levels (8.67% to 50.85%) with maintained or improved performance.

Provides 1.47-2.62x speedup in GNN inference with negligible performance loss.

Boosts GNN performance on large datasets, improving accuracy by up to 1.74%.

Abstract

Graph Neural Networks (GNNs) have demonstrated superior performance across various graph learning tasks but face significant computational challenges when applied to large-scale graphs. One effective approach to mitigate these challenges is graph sparsification, which involves removing non-essential edges to reduce computational overhead. However, previous graph sparsification methods often rely on a single global sparsity setting and uniform pruning criteria, failing to provide customized sparsification schemes for each node's complex local context. In this paper, we introduce Mixture-of-Graphs (MoG), leveraging the concept of Mixture-of-Experts (MoE), to dynamically select tailored pruning solutions for each node. Specifically, MoG incorporates multiple sparsifier experts, each characterized by unique sparsity levels and pruning criteria, and selects the appropriate experts for each node. Subsequently, MoG performs a mixture of the sparse graphs produced by different experts on the Grassmann manifold to derive an optimal sparse graph. One notable property of MoG is its entirely local nature, as it depends on the specific circumstances of each individual node. Extensive experiments on four large-scale OGB datasets and two superpixel datasets, equipped with five GNN backbones, demonstrate that MoG (I) identifies subgraphs at higher sparsity levels ($8.67\%\sim 50.85\%$), with performance equal to or better than the dense graph, (II) achieves $1.47-2.62\times$ speedup in GNN inference with negligible performance drop, and (III) boosts ``top-student'' GNN performance ($1.02\%\uparrow$ on RevGNN+\textsc{ogbn-proteins} and $1.74\%\uparrow$ on DeeperGCN+\textsc{ogbg-ppa}).