Abstract
In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text.
| Original language | English |
|---|---|
| Title of host publication | IJCAI International Joint Conference on Artificial Intelligence |
| Pages | 2274-2280 |
| Number of pages | 7 |
| DOIs | |
| Publication status | Published - 2011 |
| Externally published | Yes |
| Event | 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Catalonia, Spain Duration: 16 Jul 2011 → 22 Jul 2011 |
Other
| Other | 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 |
|---|---|
| Country | Spain |
| City | Barcelona, Catalonia |
| Period | 16/7/11 → 22/7/11 |
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ASJC Scopus subject areas
- Artificial Intelligence
Cite this
Context sensitive topic models for author influence in document networks. / Kataria, Saurabh; Mitra, Prasenjit; Caragea, Cornelia; Giles, C. Lee.
IJCAI International Joint Conference on Artificial Intelligence. 2011. p. 2274-2280.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Context sensitive topic models for author influence in document networks
AU - Kataria, Saurabh
AU - Mitra, Prasenjit
AU - Caragea, Cornelia
AU - Giles, C. Lee
PY - 2011
Y1 - 2011
N2 - In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text.
AB - In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text.
UR - http://www.scopus.com/inward/record.url?scp=84871102871&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871102871&partnerID=8YFLogxK
U2 - 10.5591/978-1-57735-516-8/IJCAI11-379
DO - 10.5591/978-1-57735-516-8/IJCAI11-379
M3 - Conference contribution
AN - SCOPUS:84871102871
SN - 9781577355120
SP - 2274
EP - 2280
BT - IJCAI International Joint Conference on Artificial Intelligence
ER -