Latent business networks mining : A probabilistic generative model
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 |
| Pages | 558-562 |
| Publication status | Published - 2012 |
Conference
| Title | 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 |
|---|---|
| Place | China |
| City | Macau |
| Period | 4 - 7 December 2012 |
Link(s)
Abstract
Though numerous research has been devoted to social network discovery and analysis, relatively little research has been conducted on business network discovery. The main contribution of our research is the development of a novel probabilistic generative model for latent business networks mining. Our experimental results confirm that the proposed method outperforms the well-known vector space based model by 24% in terms of AUC value. © 2012 IEEE.
Research Area(s)
- Business Networks Mining, Latent Dirichlet Allocation, Semi-supervised Machine Learning, Web Intelligence
Citation Format(s)
Latent business networks mining: A probabilistic generative model. / Zhang, Wenping; Lau, Raymond Y.K.; Xia, Yunqing et al.
Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012. 2012. p. 558-562 6511940.
Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012. 2012. p. 558-562 6511940.
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
