The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks
Abstract
Learning node representations in graphs is important for many applications such as link prediction, node classification, and community detection. Existing graph representation learning methods primarily target static graphs while many real-world graphs evolve over time. Complex time-varying graph structures make it challenging to learn informative node representations over time.
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Citations
Cites
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926
Inductive Representation Learning on Large Graphs
Hamilton, W. L., Ying, R., & Leskovec, J. (2017) · arXiv · doi:10.48550/arxiv.1706.02216 · Cited by 10,639
Convolutional Sequence to Sequence Learning
Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y. N. (2017) · arXiv · doi:10.48550/arxiv.1705.03122 · Cited by 2,850 · cited 1×
“First, we capture the ordering information in the temporal attention module by using position embeddings [5], {p1, .”
Method
Cited by
Temporal Graph Networks for Deep Learning on Dynamic Graphs
Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., & Bronstein, M. (2020) · arXiv · doi:10.48550/arxiv.2006.10637 · Cited by 382
TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion
Wu, J., Cao, M., Cheung, J. C. K., & Hamilton, W. L. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.462 · Cited by 167
Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction
Wen, Z., Fang, Y., Wei, P., Liu, F., Chen, Z., & Wu, M. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3592788 · Cited by 34
TPmod: A Tendency-Guided Prediction Model for Temporal Knowledge Graph Completion
Bai, L., Ma, X., Zhang, M., & Yu, W. (2021) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3443687 · Cited by 23
Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detection
Wu, Y., Gu, M., Wang, L., Lin, Y., Wang, F., & Yang, H. (2021) · arXiv · doi:10.48550/arxiv.2108.06783 · Cited by 6
Self-citation
Metadata
- Authors
- Sankar, A., Wu, Y., Gou, L., Zhang, W., & Yang, H.
- Venue
- Proceedings of the 13th International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2020-01-20
- Pages
- 519-527
- OpenAlex
- W2998116985
- Cited by
- 638
- References
- 4
- FWCI
- 28.12
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Sankar, A., Wu, Y., Gou, L., Zhang, W., & Yang, H. (2020). DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks. Proceedings of the 13th International Conference on Web Search and Data Mining. https://doi.org/10.1145/3336191.3371845
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
