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Understanding Negative Sampling in Graph Representation Learning
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“Negative sampling [Yang et al., 2020] creates negative samples s ′ by randomly corrupting true facts, which are widely used by TKGC methods to improve the expressiveness of learned representations.”
From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Yang et al., 2020
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Cites
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Cited by
Sorted by citations
Temporal Knowledge Graph Completion: A Survey
Cai, B., Xiang, Y., Gao, L., Zhang, H., Li, Y., & Li, J. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/734 · Cited by 133
Metadata
- Authors
- Yang, Z., Ding, M., Zhou, C., Yang, H., Zhou, J., & Tang, J.
- Affiliations
- Tsinghua University, Beijing, China
- DAMO Academy, Alibaba Group, Hangzhou, China
- Venue
- Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2020-08-20
- Pages
- 1666-1676
- Cited by
- 159
- References
- 3
- Funders
- National Natural Science Foundation of China (61825602)
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