LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?-Reference-Cited by-同舟云学术

LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?

Published:2024-08-24 Issue: Volume:35 Page:4350-4361
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Zhang Zeyang¹^ORCID,Wang Xin²^ORCID,Zhang Ziwei¹^ORCID,Li Haoyang¹^ORCID,Qin Yijian¹^ORCID,Zhu Wenwu²^ORCID

Affiliation:

1. DCST, Tsinghua University, Beijing, China

2. DCST, BNRist, Tsinghua University, Beijing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Beijing National Research Center for Information Science and Technology under Grant

Beijing Key Lab of Networked Multimedia

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671709

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3. Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, Vol. 35, 8 (2013), 1798--1828.

4. Graph of Thoughts: Solving Elaborate Problems with Large Language Models

5. Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell Sandhini Agarwal Ariel Herbert-Voss Gretchen Krueger Tom Henighan Rewon Child Aditya Ramesh Daniel Ziegler Jeffrey Wu Clemens Winter Chris Hesse Mark Chen Eric Sigler Mateusz Litwin Scott Gray Benjamin Chess Jack Clark Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems. 1877--1901.

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