Graphprompt: Unifying pre-training and downstream tasks for graph neural networks
Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised settin...
Saved in:
Main Authors: | LIU, Zemin, YU, Xingtong, FANG, Yuan, ZHANG, Xinming |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2023
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/8191 https://ink.library.smu.edu.sg/context/sis_research/article/9194/viewcontent/TheWebConf23_GraphPrompt.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs
by: YU, Xingtong, et al.
Published: (2024) -
MultiGPrompt for multi-task pre-training and prompting on graphs
by: YU, Xingtong, et al.
Published: (2024) -
Voucher abuse detection with prompt-based fine-tuning on graph neural networks
by: WEN, Zhihao, et al.
Published: (2023) -
Augmenting low-resource text classification with graph-grounded pre-training and prompting
by: WEN, Zhihao, et al.
Published: (2023) -
Relative and absolute location embedding for few-shot node classification on graph
by: LIU, Zemin, et al.
Published: (2021)