Identifying Link Farm Spam Pages

Identifying Link Farm Spam Pages (2005)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Baoning Wu , Brian D. Davison

Venue:	Proceedings of the 14th International World Wide Web Conference
Citations:	73 - 10 self

Versions

Version History

Metadata Version 1

User correction supplied by davison

Datum	Value	Source
TITLE	Identifying Link Farm Spam Pages	user correction
AUTHOR NAME	Baoning Wu	user correction
AUTHOR AFFIL	Department of Computer Science & Engineering, Lehigh University	user correction
AUTHOR ADDR	19 Memorial Dr. West; Bethlehem, PA 18015 USA	user correction
AUTHOR NAME	Brian D. Davison	user correction
AUTHOR AFFIL	Department of Computer Science and Engineering; Lehigh University	user correction
AUTHOR ADDR	19 Memorial Drive West; Bethlehem, PA 18015 USA	user correction
ABSTRACT	With the increasing importance of search in guiding today’s web traffic, more and more effort has been spent to create search engine spam. Since link analysis is one of the most important factors in current commercial search engines’ ranking systems, new kinds of spam aiming at links have appeared. Building link farms is one technique that can deteriorate link-based ranking algorithms. In this paper, we present algorithms for detecting these link farms automatically by first generating a seed set based on the common link set between incoming and outgoing links of Web pages and then expanding it. Links between identified pages are reweighted, providing a modified web graph to use in ranking page importance. Experimental results show that we can identify most link farm spam pages and the final ranking results are improved for almost all tested queries.	SVM HeaderParse 0.2
YEAR	2005	user correction
VENUE	Proceedings of the 14th International World Wide Web Conference	user correction
VENUE TYPE	CONFERENCE	user correction
PAGES	820--829	user correction
CITATIONS	25 found	ParsCit 1.0