The linear algebra involved in HITS and PageRank is standard, but selecting the relevant subgraph of the Web to which these algorithms should be applied is considerably less clear. The clean graph model used in HITS and PageRank, where pages are nodes with no finer characteristics other than a few scalar popularity scores, is also in question.
Pages have valuable markup structure and accompanying text.
In this talk we will discuss two enhancements to the graph selection process. We will justify, using analyses and anecdotes, that as the Web evolves from static files to dynamically generated semi-structured content, these more complex models and algorithms will become crucial to the continued success of automatic resource discovery, extraction, and annotation. Soumen Chakrabarti received his B.Tech from I.I.T. in 1991 and his M.S. and Ph.D. in Computer Science from U.C. Berkeley in 1992 and 1996. At Berkeley he worked on compilers and runtime systems for scalable parallel scientific software. He was a Research Staff Member at IBM Almaden Research Center between 1996 and 1999.