The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Representation Learning for Attributed Multiplex Heterogeneous Network
Open accessAbstract
Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed nodes/edges and cannot scale well to handle large networks. Many real-world networks consist of billions of nodes and edges of multiple types, and each node is associated with different attributes. In this paper, we formalize the problem of embedding learning for the Attributed Multiplex Heterogeneous Network and propose a unified framework to address this problem. The framework supports both transductive and inductive learning. We also give the theoretical analysis of the proposed framework, showing its connection with previous works and proving its better expressiveness. We conduct systematical evaluations for the proposed framework on four different genres of challenging datasets: Amazon, YouTube, Twitter, and Alibaba. Experimental results demonstrate that with the learned embeddings from the proposed framework, we can achieve statistically significant improvements (e.g., 5.99-28.23% lift by F1 scores; p<<0.01, t-test) over previous state-of-the-art methods for link prediction. The framework has also been successfully deployed on the recommendation system of a worldwide leading e-commerce company, Alibaba Group. Results of the offline A/B tests on product recommendation further confirm the effectiveness and efficiency of the framework in practice.
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A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
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Lv, Q., Ding, M., Liu, Q., Chen, Y., Feng, W., He, S., Zhou, C., Jiang, J., Dong, Y., & Tang, J. (2021) · Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining · doi:10.1145/3447548.3467350 · Cited by 292
Self-citationeFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks
Zhang, G., Li, Z., Huang, J., Wu, J., Zhou, C., Yang, J., & Gao, J. (2022) · ACM Transactions on Information Systems · doi:10.1145/3474379 · Cited by 177
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Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market
Peng, H., Li, J., Wang, Z., Yang, R., Liu, M., Zhang, M., Yu, P., & He, L. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3112749 · Cited by 43
Metadata
- Authors
- Cen, Y., Zou, X., Zhang, J., Yang, H., Zhou, J., & Tang, J.
- Venue
- Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2019-07-25
- Pages
- 1358-1368
- OpenAlex
- W2945266622
- Cited by
- 465
- References
- 43
- FWCI
- 25.44
- Type
- conference paper
- Language
- en
- Open access
- green
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- Cen, Y., Zou, X., Zhang, J., Yang, H., Zhou, J., & Tang, J. (2019). Representation Learning for Attributed Multiplex Heterogeneous Network. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3292500.3330964
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