All in One: Multi-Task Prompting for Graph Neural Networks-Reference-Cited by-同舟云学术

All in One: Multi-Task Prompting for Graph Neural Networks

Published:2023-08-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Sun Xiangguo¹^ORCID,Cheng Hong¹^ORCID,Li Jia²^ORCID,Liu Bo³^ORCID,Guan Jihong⁴^ORCID

Affiliation:

1. The Chinese University of Hong Kong, New Territories, Hong Kong

2. The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

3. Southeast University & Purple Mountain Laboratories, Nanjing, China

4. Tongji University, Shanghai, China

Funder

Fundamental Research Funds for the Central Universities

Guangzhou-HKUST(GZ) Joint Funding Scheme

Research Grant Council of the Hong Kong Special Administrative Region, China

Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong

NSFC

National Key R&D Program of China

Open Research Projects of Zhejiang Lab

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599256

Reference40 articles.

1. Efficient Top-k Shortest-Path Distance Queries on Large Networks by Pruned Landmark Labeling

2. Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity

3. Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell etal 2020. Language models are few-shot learners. Advances in neural information processing systems Vol. 33 (2020) 1877--1901. Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems Vol. 33 (2020) 1877--1901.

4. Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction

5. BrainNet

Cited by 31 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Edge contrastive learning for link prediction;Information Processing & Management;2024-11

2. MIGP: Metapath Integrated Graph Prompt Neural Network;Neural Networks;2024-11

3. Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation;IEEE Transactions on Knowledge and Data Engineering;2024-09

4. WhisPAr: Transferring pre-trained audio models to fine-grained classification via Prompt and Adapter;Knowledge-Based Systems;2024-09

5. Empirical and Experimental Perspectives on Big Data in Recommendation Systems: A Comprehensive Survey;Big Data Mining and Analytics;2024-09