AGS-GNN: Attribute-guided Sampling for Graph Neural Networks-Reference-Cited by-同舟云学术

AGS-GNN: Attribute-guided Sampling for Graph Neural Networks

Published:2024-08-24 Issue: Volume:97 Page:538-549
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Das Siddhartha Shankar¹^ORCID,Ferdous S M²^ORCID,Halappanavar Mahantesh M.²^ORCID,Serra Edoardo³^ORCID,Pothen Alex¹^ORCID

Affiliation:

1. Purdue University, West Lafayette, IN, USA

2. Pacific Northwest National Lab., Richland, WA, USA

3. Boise State University, Boise, ID, USA

Funder

U. S. Department of Energy, Office of Science

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671940

Reference51 articles.

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3. Jie Chen, Tengfei Ma, and Cao Xiao. 2018. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. In The 6th International Conference on Learning Representations. OpenReview.net, Vancouver, BC, Canada.

4. Jianfei Chen, Jun Zhu, and Le Song. 2018. Stochastic Training of Graph Convolutional Networks with Variance Reduction. In The 35th International Conference on Machine Learning, Jennifer G. Dy and Andreas Krause (Eds.), Vol. 80. PMLR, Stockholm, Sweden, July, 941--949.

5. Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020. Simple and Deep Graph Convolutional Networks. In The 37th International Conference on Machine Learning, Vol. 119. PMLR, Virtual Event, 1725--1735.