GNNVis: Visualize Large-Scale Data by Learning a Graph Neural Network Representation-Reference-Cited by-同舟云学术

GNNVis: Visualize Large-Scale Data by Learning a Graph Neural Network Representation

Published:2020-10-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM International Conference on Information & Knowledge Management
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Author:

Huang Yajun¹,Zhang Jingbin²,Yang Yiyang³,Gong Zhiguo⁴,Hao Zhifeng⁵

Affiliation:

1. Dingxiang Technologies, Hangzhou, China

2. University of Macau, Macau SAR, Macao

3. Guangdong University of Technology, Guangzhou, China

4. University of Macau, Macao SAR, Macao

5. Foshan University, Foshan, China

Funder

NSFC-Guangdong Joint Found

Ministry of Science and Technology of the People's Republic of China

Guangdong PKL of CPS

National Natural Science Foundation of China (NSFC)

Macau Science and Technology Development Fund (FDCT)

Guangzhou Science of Technology and Innovation Commission

Universidade de Macau

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3340531.3411987

Reference38 articles.

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4. Jianfei Chen Jun Zhu and Le Song. 2018b. Stochastic Training of Graph Convolutional Networks with Variance Reduction. In ICML. 941--949. Jianfei Chen Jun Zhu and Le Song. 2018b. Stochastic Training of Graph Convolutional Networks with Variance Reduction. In ICML. 941--949.

5. Local Multidimensional Scaling for Nonlinear Dimension Reduction, Graph Drawing, and Proximity Analysis

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