Whole-Graph Embedding and Adversarial Attacks for Life Sciences-Reference-Cited by-同舟云学术

Whole-Graph Embedding and Adversarial Attacks for Life Sciences

Published:2022 Issue: Volume: Page:1-21
ISSN:
Container-title:Trends in Biomathematics: Stability and Oscillations in Environmental, Social, and Biological Models
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Author:

Maddalena L.,Giordano M.,Manzo M.,Guarracino M. R.

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-12515-7_1

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3. Yunsheng Bai, Hao Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Ting Chen, Yizhou Sun, and Wei Wang. Unsupervised inductive graph-level representation learning via graph-graph proximity. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI’19, page 1988–1994. AAAI Press, 2019.

4. Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, S. V. N. Vishwanathan, Alex J. Smola, and Hans-Peter Kriegel. Protein function prediction via graph kernels. Bioinformatics, 21(suppl 1):i47–i56, 06 2005.

5. Ulrik Brandes. On variants of shortest-path betweenness centrality and their generic computation. Social Networks, 30(2):136–145, 2008.

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