Simple Graph Condensation
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909261240467456 |
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| author | Xiao, Zhenbang Wang, Yu Liu, Shunyu Wang, Huiqiong Song, Mingli Zheng, Tongya |
| author_facet | Xiao, Zhenbang Wang, Yu Liu, Shunyu Wang, Huiqiong Song, Mingli Zheng, Tongya |
| contents | The burdensome training costs on large-scale graphs have aroused significant interest in graph condensation, which involves tuning Graph Neural Networks (GNNs) on a small condensed graph for use on the large-scale original graph. Existing methods primarily focus on aligning key metrics between the condensed and original graphs, such as gradients, output distribution and trajectories of GNNs, yielding satisfactory performance on downstream tasks. However, these complex metrics necessitate intricate external parameters and can potentially disrupt the optimization process of the condensation graph, making the condensation process highly demanding and unstable. Motivated by the recent success of simplified models across various domains, we propose a simplified approach to metric alignment in graph condensation, aiming to reduce unnecessary complexity inherited from intricate metrics. We introduce the Simple Graph Condensation (SimGC) framework, which aligns the condensed graph with the original graph from the input layer to the prediction layer, guided by a pre-trained Simple Graph Convolution (SGC) model on the original graph. Importantly, SimGC eliminates external parameters and exclusively retains the target condensed graph during the condensation process. This straightforward yet effective strategy achieves a significant speedup of up to 10 times compared to existing graph condensation methods while performing on par with state-of-the-art baselines. Comprehensive experiments conducted on seven benchmark datasets demonstrate the effectiveness of SimGC in prediction accuracy, condensation time, and generalization capability. Our code is available at https://github.com/BangHonor/SimGC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14951 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Simple Graph Condensation Xiao, Zhenbang Wang, Yu Liu, Shunyu Wang, Huiqiong Song, Mingli Zheng, Tongya Machine Learning Artificial Intelligence Social and Information Networks The burdensome training costs on large-scale graphs have aroused significant interest in graph condensation, which involves tuning Graph Neural Networks (GNNs) on a small condensed graph for use on the large-scale original graph. Existing methods primarily focus on aligning key metrics between the condensed and original graphs, such as gradients, output distribution and trajectories of GNNs, yielding satisfactory performance on downstream tasks. However, these complex metrics necessitate intricate external parameters and can potentially disrupt the optimization process of the condensation graph, making the condensation process highly demanding and unstable. Motivated by the recent success of simplified models across various domains, we propose a simplified approach to metric alignment in graph condensation, aiming to reduce unnecessary complexity inherited from intricate metrics. We introduce the Simple Graph Condensation (SimGC) framework, which aligns the condensed graph with the original graph from the input layer to the prediction layer, guided by a pre-trained Simple Graph Convolution (SGC) model on the original graph. Importantly, SimGC eliminates external parameters and exclusively retains the target condensed graph during the condensation process. This straightforward yet effective strategy achieves a significant speedup of up to 10 times compared to existing graph condensation methods while performing on par with state-of-the-art baselines. Comprehensive experiments conducted on seven benchmark datasets demonstrate the effectiveness of SimGC in prediction accuracy, condensation time, and generalization capability. Our code is available at https://github.com/BangHonor/SimGC. |
| title | Simple Graph Condensation |
| topic | Machine Learning Artificial Intelligence Social and Information Networks |
| url | https://arxiv.org/abs/2403.14951 |