Graph Minimally-supervised Learning-Reference-Cited by-同舟云学术

Graph Minimally-supervised Learning

Published:2022-02-11 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
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Author:

Ding Kaize¹,Li Jundong²,Chawla Nitesh³,Liu Huan¹

Affiliation:

1. Arizona State University, Tempe, AZ, USA

2. University of Virginia, Charlottesville, VA, USA

3. University of Notre Dame, Notre Dame, IN, USA

Funder

Office of Naval Research

Army Research Office

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3488560.3501390

Reference42 articles.

1. Bielak , P. , Kajdanowicz , T. , and Chawla , N. V . Graph barlow twins: A self-supervised representation learning framework for graphs. arXiv preprint arXiv:2106.02466 ( 2021 ). Bielak, P., Kajdanowicz, T., and Chawla, N. V. Graph barlow twins: A self-supervised representation learning framework for graphs. arXiv preprint arXiv:2106.02466 (2021).

2. Chen , T. , Kornblith , S. , Norouzi , M. , and Hinton , G . A simple framework for contrastive learning of visual representations . In ICML ( 2020 ). Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. A simple framework for contrastive learning of visual representations. In ICML (2020).

3. Inductive Anomaly Detection on Attributed Networks

4. Deep Anomaly Detection on Attributed Networks

5. Interactive Anomaly Detection on Attributed Networks

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