The DOI matches its Crossref record.
Joint latent topic models for text and citations
Abstract
In this work, we address the problem of joint modeling of text and citations in the topic modeling framework. We present two different models called the Pairwise-Link-LDA and the Link-PLSA-LDA models.
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Citations
Cited by
Empirical study of topic modeling in Twitter
Hong, L., & Davison, B. D. (2010) · Proceedings of the First Workshop on Social Media Analytics · doi:10.1145/1964858.1964870 · Cited by 1,120
A Survey of Link Prediction in Social Networks
Hasan, M. A., & Zaki, M. J. (2011) · Social Network Data Analytics · doi:10.1007/978-1-4419-8462-3_9 · Cited by 566
A Survey of Topic Modeling in Text Mining
Alghamdi, R., & Alfalqi, K. (2015) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2015.060121 · Cited by 377
A survey of community search over big graphs
Fang, Y., Huang, X., Qin, L., Zhang, Y., Zhang, W., Cheng, R., & Lin, X. (2020) · The VLDB Journal · doi:10.1007/s00778-019-00556-x · Cited by 339
Effective community search for large attributed graphs
Fang, Y., Cheng, R., Luo, S., & Hu, J. (2016) · Proceedings of the VLDB Endowment · doi:10.14778/2994509.2994538 · Cited by 317
Metadata
- Authors
- Nallapati, R. M., Ahmed, A., Xing, E. P., & Cohen, W. W.
- Venue
- Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2008-08-24
- Pages
- 542-550
- OpenAlex
- W2165636119
- Cited by
- 408
- References
- 0
- FWCI
- 14.39
- Type
- conference paper
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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- CoChat. (2026). Citation check: Joint latent topic models for text and citations. https://app.cochat.ai/papers/10.1145/1401890.1401957
- The paper
- Nallapati, R. M., Ahmed, A., Xing, E. P., & Cohen, W. W. (2008). Joint latent topic models for text and citations. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/1401890.1401957
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
