Name
Abstract | ||
|---|---|---|
It is well known that Web users create links with different intentions. However, a key question, which is not well studied, is how to categorize the links and how to quantify the strength of the influence of a Web page on another if there is a link between the two linked Web pages. In this paper, we focus on the problem of link semantics analysis, and propose a novel supervised learning approach to build a model, based on a training link-labeled and link-weighted graph where a link-label represents the category of a link and a link-weight represents the influence of one web page on the other in a link. Based on the model built, we categorize links and quantify the influence of Web pages on the others in a large graph in the same application domain. We discuss our proposed approach, namely pairwise restricted Boltzmann machines (PRBMs), and conduct extensive experimental studies to demonstrate the effectiveness of our approach using large real datasets. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICDM.2009.116 | Proceedings - IEEE International Conference on Data Mining, ICDM |
Keywords | Field | DocType |
supervised learning approach,web page,link-weighted graph,learning (artificial intelligence),link semantic analysis,large real datasets,pairwise restricted boltzmann machines,link analysis,different intention,large graph,link semantics analysis,link-labeled graph,internet,application domain,topic distributions,graph theory,web user,web pages,support vector machines,data mining,learning artificial intelligence,supervised learning,indexes,boltzmann machine,correlation | Graph theory,Data mining,Web page,Computer science,Link analysis,Supervised learning,Web modeling,Artificial intelligence,Application domain,Semantics,Machine learning,The Internet | Conference |
ISSN | ISBN | Citations |
1550-4786 E-ISBN : 978-0-7695-3895-2 | 978-0-7695-3895-2 | 5 |
PageRank | References | Authors |
0.43 | 10 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Tang | 1 | 5871 | 300.22 |
| Jing Zhang | 2 | 1281 | 55.47 |
| Jeffrey Xu Yu | 3 | 7018 | 464.96 |
| Keke Cai | 4 | 5 | 0.43 |
| Keke Cai | 5 | 243 | 15.36 |
| Rui Ma | 6 | 100 | 20.94 |
| Li Zhang | 7 | 2286 | 151.94 |
| Zhong Su | 8 | 2282 | 110.39 |