Subgraph-level Universal Prompt Tuning

arXiv:2402.10380v1 Announce Type: new
Abstract: In the evolving landscape of machine learning, the adaptation of pre-trained models through prompt tuning has become increasingly prominent. This trend is particularly observable in the graph domain, where diverse pre-training strategies present unique challenges in developing effective prompt-based tuning methods for graph neural networks. Previous approaches have been limited, focusing on specialized prompting functions tailored to models with edge prediction pre-training tasks. These methods, however, suffer from a lack of generalizability across different pre-training …

abstract arxiv become challenges cs.ai cs.lg diverse domain graph graph neural networks landscape machine machine learning networks neural networks observable pre-trained models pre-training prompt prompt tuning strategies through training trend type