Global Vectors for Node Representations-Reference-Cited by-同舟云学术

Global Vectors for Node Representations

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:The World Wide Web Conference on - WWW '19
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Author:

Brochier Robin¹,Guille Adrien²,Velcin Julien²

Affiliation:

1. Université de Lyon, Lyon 2, ERIC EA3083, Digital Scientific Research Technology

2. Université de Lyon, Lyon 2, ERIC EA3083

Publisher

ACM Press

Reference18 articles.

1. Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence35, 8(2013), 1798-1828.

2. Scott Deerwester, Susan T. Dumais, George W. Furnas, Thomas K. Landauer, and Richard Harshman. 1990. Indexing by latent semantic analysis. JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE41, 6(1990), 391-407.

3. Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 135-144.

4. Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin. 2008. LIBLINEAR: A library for large linear classification. Journal of machine learning research9, Aug (2008), 1871-1874.

5. Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 855-864.

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