The DOI matches its Crossref record; 140 of the 140 references it cites were found in a registry or an index.
A survey of heterogeneous information network analysis
Open accessAbstract
Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as homogeneous information networks, without distinguishing different types of objects and links in the networks. Recently, more and more researchers begin to consider these interconnected, multi-typed data as heterogeneous information networks, and develop structural analysis approaches by leveraging the rich semantic meaning of structural types of objects and links in the networks. Compared to widely studied homogeneous information network, the heterogeneous information network contains richer structure and semantic information, which provides plenty of opportunities as well as a lot of challenges for data mining. In this paper, we provide a survey of heterogeneous information network analysis. We will introduce basic concepts of heterogeneous information network analysis, examine its developments on different data mining tasks, discuss some advanced topics, and point out some future research directions.
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Cited by
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Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., & Yu, P. S. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313562 · Cited by 3,191
Self-citationHeterogeneous Information Network Embedding for Recommendation
Shi, C., Hu, B., Zhao, W. X., & Yu, P. S. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2833443 · Cited by 1,204
Self-citationLeveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model
Hu, B., Shi, C., Zhao, W. X., & Yu, P. S. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3219965 · Cited by 674
Self-citationLearning Intents behind Interactions with Knowledge Graph for Recommendation
Wang, X., Huang, T., Wang, D., Yuan, Y., Liu, Z., He, X., & Chua, T.-S. (2021) · Proceedings of the Web Conference 2021 · doi:10.1145/3442381.3450133 · Cited by 578
Xia, F., Sun, K., Yu, S., Aziz, A., Wan, L., Pan, S., & Liu, H. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3076021 · Cited by 473
Metadata
- Authors
- Shi, C., Li, Y., Zhang, J., Sun, Y., & Yu, P. S.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2017-01-01
- Volume
- 29
- Issue
- 1
- Pages
- 17-37
- ISSN
- 1041-4347 (ISSN-L)
- 1558-2191
- 2326-3865
- OpenAlex
- W2963919031
- Cited by
- 1,112
- References
- 140
- FWCI
- 67.50
- Type
- journal article
- Language
- en
- Open access
- green
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- Shi, C., Li, Y., Zhang, J., Sun, Y., & Yu, P. S. (2017). A survey of heterogeneous information network analysis. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/tkde.2016.2598561
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