Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning

• URL: http://arxiv.org/abs/2410.05697v1
• Date: Tue, 8 Oct 2024 05:27:34 GMT
• Title: Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning
• Authors: Lequan Lin, Dai Shi, Andi Han, Zhiyong Wang, Junbin Gao,
• Abstract summary: This work introduces a graph-conditioned latent diffusion framework (GNN-Diff) to generate high-performing GNNs. We validate our method through 166 experiments across four graph tasks: node classification on small, large, and long-range graphs, as well as link prediction.
• Score: 33.948899558876604
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Neural Networks (GNNs) are proficient in graph representation learning and achieve promising performance on versatile tasks such as node classification and link prediction. Usually, a comprehensive hyperparameter tuning is essential for fully unlocking GNN's top performance, especially for complicated tasks such as node classification on large graphs and long-range graphs. This is usually associated with high computational and time costs and careful design of appropriate search spaces. This work introduces a graph-conditioned latent diffusion framework (GNN-Diff) to generate high-performing GNNs based on the model checkpoints of sub-optimal hyperparameters selected by a light-tuning coarse search. We validate our method through 166 experiments across four graph tasks: node classification on small, large, and long-range graphs, as well as link prediction. Our experiments involve 10 classic and state-of-the-art target models and 20 publicly available datasets. The results consistently demonstrate that GNN-Diff: (1) boosts the performance of GNNs with efficient hyperparameter tuning; and (2) presents high stability and generalizability on unseen data across multiple generation runs. The code is available at https://github.com/lequanlin/GNN-Diff.

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