A Simple but Effective Approach for Unsupervised Few-Shot Graph Classification-Reference-Cited by-同舟云学术

A Simple but Effective Approach for Unsupervised Few-Shot Graph Classification

Published:2024-05-13 Issue: Volume:55 Page:4249-4259
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Container-title:Proceedings of the ACM Web Conference 2024
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Author:

Liu Yonghao¹^ORCID,Huang Lan¹^ORCID,Cao Bowen¹^ORCID,Li Ximing¹^ORCID,Giunchiglia Fausto²^ORCID,Feng Xiaoyue¹^ORCID,Guan Renchu¹^ORCID

Affiliation:

1. College of Computer Science and Technology, Jilin University, Changchun, China

2. Department of Information Engineering and Computer Science, University of Trento, Trento, Italy

Funder

National Key Research and Development Program of China

European Union?s Horizon 2020 FET Proactive Project

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645587

Reference70 articles.

1. Jinheon Baek, Dong Bok Lee, and Sung Ju Hwang. 2020. Learning to extrapolate knowledge: Transductive few-shot out-of-graph link prediction. In NeurIPS. 546--560.

2. Rademacher and Gaussian complexities: Risk bounds and structural results;Bartlett Peter L;Journal of Machine Learning Research 3,2002

3. Karsten M Borgwardt and Hans-Peter Kriegel. 2005. Shortest-path kernels on graphs. In ICDM.

4. Diana Cai, Rishit Sheth, Lester Mackey, and Nicolo Fusi. 2020. Weighted metalearning. arXiv preprint arXiv:2003.09465 (2020).

5. Jatin Chauhan Deepak Nathani and Manohar Kaul. 2020. Few-shot learning on graphs via super-classes based on graph spectral measures. In ICLR.

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