A Novel Prompt Tuning for Graph Transformers: Tailoring Prompts to Graph Topologies-Reference-Cited by-同舟云学术

A Novel Prompt Tuning for Graph Transformers: Tailoring Prompts to Graph Topologies

Published:2024-08-24 Issue: Volume:33 Page:3116-3127
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Wang Jingchao¹^ORCID,Deng Zhengnan²^ORCID,Lin Tongxu³^ORCID,Li Wenyuan¹^ORCID,Ling Shaobin⁴^ORCID

Affiliation:

1. Guangdong University of Technology, Guangzhou, China

2. Guangdong University of Technology, Guangzhou, --- Select One ---, China

3. Beijing University of Posts and Telecommunications, Beijing, China

4. South China Normal University, Guangzhou, --- Select One ---, China

Funder

the National Natural Science Foundation of China

Natural Sciences and Engineering Research Council of Canada (NSERC)

the National Natural Science Foundation of China (NSFC)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671804

Reference41 articles.

1. Xavier Bresson and Thomas Laurent. 2017. Residual gated graph convnets. arXiv:1711.07553 (2017).

2. Dexiong Chen, Laurent Jacob, and Julien Mairal. 2020. Convolutional kernel networks for graph-structured data. In International Conference on Machine Learning. PMLR, 1576--1586.

3. SIAM Journal on computing;Chiba Norishige,1985

4. Principal neighbourhood aggregation for graph nets;Corso Gabriele;Advances in Neural Information Processing Systems,2020

5. Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly et al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929 (2020).