The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
RankClus: integrating clustering with ranking for heterogeneous information network analysis
Open accessAbstract
As information networks become ubiquitous, extracting knowledge from information networks has become an important task. Both ranking and clustering can provide overall views on information network data, and each has been a hot topic by itself. However, ranking objects globally without considering which clusters they belong to often leads to dumb results, e.g., ranking database and computer architecture conferences together may not make much sense. Similarly, clustering a huge number of objects (e.g., thousands of authors) in one huge cluster without distinction is dull as well.
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Shi, J., & Malik, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868688 · Cited by 15,502
An index to quantify an individual’s scientific research output
Hirsch, J. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0507655102 · Cited by 11,669
A tutorial on spectral clustering
von Luxburg, U. (2007) · Statistics and Computing · doi:10.1007/s11222-007-9033-z · Cited by 10,349
Authoritative sources in a hyperlinked environment
Kleinberg, J. M. (1999) · Journal of the ACM · doi:10.1145/324133.324140 · Cited by 8,959
Bilmes, J. A. (1998) · Cited by 2,451
SimRank: a measure of structural-context similarity
Jeh, G., & Widom, J. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775126 · Cited by 1,925
Scatter/Gather: A Cluster-based Approach to Browsing Large Document Collections
Cutting, D. R., Karger, D. R., Pedersen, J. O., & Tukey, J. W. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130362 · Cited by 1,419
Combating Web Spam with TrustRank
Gyöngyi, Z., Garcia-Molina, H., & Pedersen, J. (2004) · Proceedings 2004 VLDB Conference · doi:10.1016/b978-012088469-8.50052-8 · Cited by 1,085
Scatter/Gather: a cluster-based approach to browsing large document collections
Cutting, D. R., Karger, D. R., Pedersen, J. O., & Tukey, J. W. (1992) · Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '92 · doi:10.1145/133160.133214 · Cited by 953
Cited by
Ranking-based clustering of heterogeneous information networks with star network schema
Sun, Y., Yu, Y., & Han, J. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557107 · Cited by 513
Self-citationMethodologies for Cross-Domain Data Fusion: An Overview
Zheng, Y. (2015) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2015.2465959 · Cited by 454
Liang, Y., Meng, F., Zhang, Y., Chen, Y., Xu, J., & Zhou, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17575 · Cited by 63
Metadata
- Authors
- Sun, Y., Han, J., Zhao, P., Yin, Z., Cheng, H., & Wu, T.
- Venue
- Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
- Publisher
- ACM
- Published
- 2009-03-24
- Pages
- 565-576
- OpenAlex
- W2168175183
- Cited by
- 382
- References
- 15
- FWCI
- 21.91
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Sun, Y., Han, J., Zhao, P., Yin, Z., Cheng, H., & Wu, T. (2009). RankClus: integrating clustering with ranking for heterogeneous information network analysis. Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology. https://doi.org/10.1145/1516360.1516426
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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.
