GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929374702338048 |
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| author | Hou, Zhenyu Li, Haozhan Cen, Yukuo Tang, Jie Dong, Yuxiao |
| author_facet | Hou, Zhenyu Li, Haozhan Cen, Yukuo Tang, Jie Dong, Yuxiao |
| contents | Graph self-supervised learning (SSL) holds considerable promise for mining and learning with graph-structured data. Yet, a significant challenge in graph SSL lies in the feature discrepancy among graphs across different domains. In this work, we aim to pretrain one graph neural network (GNN) on a varied collection of graphs endowed with rich node features and subsequently apply the pretrained GNN to unseen graphs. We present a general GraphAlign method that can be seamlessly integrated into the existing graph SSL framework. To align feature distributions across disparate graphs, GraphAlign designs alignment strategies of feature encoding, normalization, alongside a mixture-of-feature-expert module. Extensive experiments show that GraphAlign empowers existing graph SSL frameworks to pretrain a unified and powerful GNN across multiple graphs, showcasing performance superiority on both in-domain and out-of-domain graphs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02953 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment Hou, Zhenyu Li, Haozhan Cen, Yukuo Tang, Jie Dong, Yuxiao Machine Learning Graph self-supervised learning (SSL) holds considerable promise for mining and learning with graph-structured data. Yet, a significant challenge in graph SSL lies in the feature discrepancy among graphs across different domains. In this work, we aim to pretrain one graph neural network (GNN) on a varied collection of graphs endowed with rich node features and subsequently apply the pretrained GNN to unseen graphs. We present a general GraphAlign method that can be seamlessly integrated into the existing graph SSL framework. To align feature distributions across disparate graphs, GraphAlign designs alignment strategies of feature encoding, normalization, alongside a mixture-of-feature-expert module. Extensive experiments show that GraphAlign empowers existing graph SSL frameworks to pretrain a unified and powerful GNN across multiple graphs, showcasing performance superiority on both in-domain and out-of-domain graphs. |
| title | GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.02953 |