Link spam detection using smooth classification function

Details Link spam detection using smooth classification function Link spam detection using smooth classification function

2011-05-10

2011-05-10

Sparks, Donald (Department: 2129)

Data processing: artificial intelligence

Knowledge processing system

Knowledge representation and reasoning technique

C706S045000, C706S020000

Reexamination Certificate

active

07941391

ABSTRACT:
A collection of web pages is considered as a directed graph in which the pages themselves are nodes and the hyperlinks between the pages are directed edges in the graph. A trusted entity identifies training examples for spam pages and normal pages. A random walk is conducted through the directed graph that includes the collection of web pages and the stationary probabilities, and transitional probabilities, among the nodes in the directed graph are obtained. A classifier training component estimates a classification function that changes slowly on densely connected subgraphs within the directed graph. The classification function assigns a value to each of the nodes in the directed graph and identifies them as spam or normal pages based upon whether the value meets a given function threshold value.

REFERENCES:
patent: 7010526 (2006-03-01), Denesuk et al.
patent: 2004/0260776 (2004-12-01), Starbuck et al.
patent: 2005/0060297 (2005-03-01), Najork
patent: 2006/0004748 (2006-01-01), Ramarathnam et al.
patent: 2006/0069667 (2006-03-01), Manasse et al.
patent: 2006/0095416 (2006-05-01), Barkhin et al.
patent: 2006/0122957 (2006-06-01), Chen
patent: 2006/0168056 (2006-07-01), Gandhi et al.
patent: 2007/0038600 (2007-02-01), Guha
patent: 2007/0078939 (2007-04-01), Kallen
patent: 0462292 (2004-12-01), None
Zhou et al., D., “Learning from Labeled and Unlabeled Data on a Directed Graph”, International Conference on Machine Learning, pp. 1036-1043 , 2005.
Zhou et al., D., “Semi-supervised Learning by Maximizing Smoothness”, Journal of Machine Learning Research, pp. 1-23, 2004.
Zhou et al., D., “Learningfrom Labeled and Unlabeled Data Using Random Walks”, DAGM 2004, pp. 237-244, 2004.
Zhou et al., D., “A Regularization Framework for Learning from Graph Data”, Workshop on Statistical Relational Learning of International Conference on Machine Learning, pp. 1-6, 2004.
International Search Report, PCT/US2008/061637 dated Aug. 26, 2008.
Zoltan Gyongyi et al., “Link Spam Detection Based on Mass Estimation” Oct. 31, 2005, Technical Report pp. 1-21.
Andras A. Benczur et al., “SpamRank-Fully Automatic Link Spam Detection-Work in Progress” Support from NKFP-2/0017/2002 project Data Riddle and Various ETIK, OTKA and AKP grants.
Zoltan Gyongyi et al. , “Combining Web Spam with TrustRank” Proceedings of the 30thVLDB Conference, Toronto, Canada, 2004, pp. 576-587.
Luca Bucchetti et al., “Link-Based Characterization and Detection of Web Spam” AIRWEB 06, Aug. 10, 2006 Seattle, WA, USA. 8 pages.

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