Unsupervised Heterogeneous Graph Rewriting Attack via Node Clustering-Reference-Cited by-同舟云学术

Unsupervised Heterogeneous Graph Rewriting Attack via Node Clustering

Published:2024-08-24 Issue: Volume:34 Page:3057-3068
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wang Haosen¹^ORCID,Xu Can²^ORCID,Shi Chenglong³^ORCID,Zheng Pengfei⁴^ORCID,Zhang Shiming⁵^ORCID,Cheng Minhao⁶^ORCID,Chen Hongyang⁴^ORCID

Affiliation:

1. Southeast University & Zhejiang Lab, Nanjing, China

2. East China Normal University, Shanghai, China

3. Southeast University, Nanjing, China

4. Zhejiang Lab, Hangzhou, China

5. University of Science and Technology of China, Hefei, China

6. Pennsylvania State University, Philadelphia, PA, USA

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671716

Reference56 articles.

1. Aleksandar Bojchevski and Stephan Günnemann. 2019. Adversarial attacks on node embeddings via graph poisoning. In International Conference on Machine Learning. PMLR, 695--704.

2. Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs

3. Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, and James Cheng. 2022. Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25--29, 2022. https://openreview. net/forum?id=wkMG8cdvh7-

4. Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation

5. MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding