Title :
Topic Distributions over Links on Web
Author :
Tang, Jie ; Zhang, Jing ; Yu, Jeffrey Xu ; Yang, Zi ; Cai, Keke ; Ma, Rui ; Zhang, Li ; Su, Zhong
Author_Institution :
Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
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.
Keywords :
Internet; graph theory; learning (artificial intelligence); Web page; link semantics analysis; link-labeled graph; link-weighted graph; pairwise restricted Boltzmann machines; supervised learning approach; topic distributions; Computer science; Data engineering; Data mining; Indexing; Information retrieval; Research and development management; Social network services; Supervised learning; Systems engineering and theory; Web pages; Link analysis; Link semantic analysis; Pairwise restricted boltzmann machines;
Conference_Titel :
Data Mining, 2009. ICDM '09. Ninth IEEE International Conference on
Conference_Location :
Miami, FL
Print_ISBN :
978-1-4244-5242-2
Electronic_ISBN :
1550-4786
DOI :
10.1109/ICDM.2009.116
