Rethinking Random Walk in Graph Representation Learning-Reference-Cited by-同舟云学术

Rethinking Random Walk in Graph Representation Learning

Published:2023-06-04 Issue: Volume: Page:
ISSN:
Container-title:ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
language:
Short-container-title:

Author:

Zeng Dingyi¹,Chen Wenyu¹,Liu Wanlong¹,Zhou Li¹,Qu Hong¹

Affiliation:

1. University of Electronic Science and Technology of China,School of Computer Science and Engineering

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10094559/10094560/10096316.pdf?arnumber=10096316

Reference36 articles.

1. The Surprising Power of Graph Neural Networks with Random Node Initialization

2. Semi-supervised classification with graph convolutional networks;kipf;5th International Conference on Learning Representations ICLR 2017,2017

3. Relational pooling for graph representations;murphy;International Conference on Machine Learning,2019

4. Graph neural networks with learnable structural and positional representations;dwivedi,2021

5. Improving graph neural network expressivity via subgraph isomorphism counting;bouritsas;IEEE Transactions on Pattern Analysis and Machine Intelligence,2022

Cited by 3 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. MLPs Compass: What is Learned When MLPs are Combined with PLMs?;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

2. Abbreviated Weighted Graph in Multi-Agent Reinforcement Learning;PRICAI 2023: Trends in Artificial Intelligence;2023-11-10

3. FastSNN: A CUDA-Based Programming Framework for Rapid Training SNNs;2023 International Annual Conference on Complex Systems and Intelligent Science (CSIS-IAC);2023-10-20