DiffusionE: Reasoning on Knowledge Graphs via Diffusion-based Graph Neural Networks-Reference-Cited by-同舟云学术

DiffusionE: Reasoning on Knowledge Graphs via Diffusion-based Graph Neural Networks

Published:2024-08-24 Issue: Volume:75 Page:222-230
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Cao Zongsheng¹^ORCID,Li Jing²^ORCID,Wang Zigan³^ORCID,Li Jinliang⁴^ORCID

Affiliation:

1. University of Chinese Academy of Sciences, Beijing, China

2. School of Economics and Management, Tsinghua University, Haidian, Beijing, China

3. Shenzhen International Graduate School & School of Economics and Management, Tsinghua University, Shenzhen, Guangdong, China

4. School of Economics and Management, Tsinghua University, Beijing, China

Funder

NSFC HY Working Fund

National Key R&D. Program of China

nstitute for Industrial Innovation and Finance (IIIF), Tsinghua University, the Hong Kong General Research Fund

Tsinghua SIGS Ba- sic Support Fund

Tsinghua SIGS Re- search Support Fund

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671997

Reference44 articles.

1. Towards Generating Hop-constrained s-t Simple Path Graphs

2. Jie Chen Tengfei Ma and Cao Xiao. 2018. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. In ICLR.

3. Kewei Cheng Jiahao Liu Wei Wang and Yizhou Sun. 2022. RLogic: Recursive Logical Rule Learning from Knowledge Graphs. In SIGKDD. 179--189.

4. Wei-Lin Chiang Xuanqing Liu Si Si Yang Li Samy Bengio and Cho-Jui Hsieh. 2019. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. In SIGKDD. 257--266.

5. Rajarshi Das Shehzaad Dhuliawala Manzil Zaheer Luke Vilnis Ishan Durugkar Akshay Krishnamurthy Alex Smola and Andrew McCallum. 2018. Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning. In ICLR.