One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908383165022208 |
|---|---|
| author | Liu, Jingzhe Mao, Haitao Chen, Zhikai Li, Bingheng Fan, Wenqi Ju, Mingxuan Zhao, Tong Shah, Neil Tang, Jiliang |
| author_facet | Liu, Jingzhe Mao, Haitao Chen, Zhikai Li, Bingheng Fan, Wenqi Ju, Mingxuan Zhao, Tong Shah, Neil Tang, Jiliang |
| contents | Graph Neural Networks (GNNs) have emerged as a powerful tool to capture intricate network patterns, achieving success across different domains. However, existing GNNs require careful domain-specific architecture designs and training from scratch on each dataset, leading to an expertise-intensive process with difficulty in generalizing across graphs from different domains. Therefore, it can be hard for practitioners to infer which GNN model can generalize well to graphs from their domains. To address this challenge, we propose a novel cross-domain pretraining framework, "one model for one graph," which overcomes the limitations of previous approaches that failed to use a single GNN to capture diverse graph patterns across domains with significant gaps. Specifically, we pretrain a bank of expert models, with each one corresponding to a specific dataset. When inferring to a new graph, gating functions choose a subset of experts to effectively integrate prior model knowledge while avoiding negative transfer. Extensive experiments consistently demonstrate the superiority of our proposed method on both link prediction and node classification tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00315 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Liu, Jingzhe Mao, Haitao Chen, Zhikai Li, Bingheng Fan, Wenqi Ju, Mingxuan Zhao, Tong Shah, Neil Tang, Jiliang Machine Learning Artificial Intelligence Graph Neural Networks (GNNs) have emerged as a powerful tool to capture intricate network patterns, achieving success across different domains. However, existing GNNs require careful domain-specific architecture designs and training from scratch on each dataset, leading to an expertise-intensive process with difficulty in generalizing across graphs from different domains. Therefore, it can be hard for practitioners to infer which GNN model can generalize well to graphs from their domains. To address this challenge, we propose a novel cross-domain pretraining framework, "one model for one graph," which overcomes the limitations of previous approaches that failed to use a single GNN to capture diverse graph patterns across domains with significant gaps. Specifically, we pretrain a bank of expert models, with each one corresponding to a specific dataset. When inferring to a new graph, gating functions choose a subset of experts to effectively integrate prior model knowledge while avoiding negative transfer. Extensive experiments consistently demonstrate the superiority of our proposed method on both link prediction and node classification tasks. |
| title | One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.00315 |