Powerful graph of graphs neural network for structured entity analysis-Reference-Cited by-同舟云学术

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Powerful graph of graphs neural network for structured entity analysis

Published:2021-06-04 Issue: Volume: Page:
ISSN:1386-145X
Container-title:World Wide Web
language:en
Short-container-title:World Wide Web

Author:

Wang Hanchen,Lian Defu,Liu Wanqi,Wen Dong,Chen Chen,Wang Xiaoyang

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Link

https://link.springer.com/content/pdf/10.1007/s11280-021-00900-8.pdf

Reference49 articles.

1. Borgwardt, K.M., Ong, C.S., Schönauer, S., Vishwanathan, S.V.N., Smola, A.J., Kriegel, H.: Protein function prediction via graph kernels. In: Proceedings Thirteenth International Conference on Intelligent Systems for Molecular Biology 2005, 25-29 June 2005, pp 47–56, Detroit, MI USA (2005)

2. Cai, J.Y., Fürer, M., Immerman, N.: An optimal lower bound on the number of variables for graph identification. Combinatorica 12(4), 389–410 (1992)

3. Chu, X., Lin, Y., Wang, Y., Wang, L., Wang, J., Gao, J.: Mlrda: a multi-task semi-supervised learning framework for drug-drug interaction prediction. In: IJCAI (2019)

4. D’Agostino, G., Scala, A.: Networks of Networks: the Last Frontier of Complexity, vol. 340. Springer, New York (2014)

5. Debnath, A.K., Lopez de Compadre, R.L., Debnath, G., Shusterman, A.J., Hansch, C.: Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity. J. Med. Chem. 34(2), 786–797 (1991)

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