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Harmonic functions based semi-supervised learning for web spam detection

Published: 21 March 2011 Publication History

Abstract

In web spam detection, we propose a new semi-supervised learning algorithm named HFSSL (harmonic functions based semi-supervised learning). In our method, labeled and unlabeled web pages are represented as vertices in a weighted graph. The learning problem is then modeled as a Gaussian random field on this graph, where the mean of the field is characterized by harmonic functions, which can be efficiently obtained using matrix methods. The experiments on standard WEBSPAM-UK2006 show that our algorithm is effective.

References

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Kleinberg J. M. Authoritative sources in a hyperlinked environment. In SODA '98, Philadelphia, PA, USA, 1998, 668--677.
[2]
Geng, G., Li, Q., Zhang, X. Link based small sample learning for web spam detection. In WWW 2009, Madrid, Spain, April 2009.
[3]
Zhou, Z. H., Li, M. Tri-training: exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 2005, 17, 1529--1541.
[4]
Weiss, Y., Freeman, W. T. Correctness of belief propagation in Gaussian graphical models of arbitrary topology. Neural Computation, 2001, 13, 2173--2200.

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  • (2024)A Survey on the Applications of Semi-supervised Learning to Cyber-securityACM Computing Surveys10.1145/365764756:10(1-41)Online publication date: 22-Jun-2024
  • (2024)Exploring Algorithmic Paradigms in Message Classification: Insights from the Enron E-mail DatasetAdvances in Information Communication Technology and Computing10.1007/978-981-97-6103-6_3(27-40)Online publication date: 2-Oct-2024
  • (2023)Email spam detection using hierarchical attention hybrid deep learning methodExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120977233:COnline publication date: 15-Dec-2023
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cover image ACM Conferences
SAC '11: Proceedings of the 2011 ACM Symposium on Applied Computing
March 2011
1868 pages
ISBN:9781450301138
DOI:10.1145/1982185
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 March 2011

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Author Tags

  1. harmonic functions
  2. semi-supervised learning
  3. web spam

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SAC'11
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SAC'11: The 2011 ACM Symposium on Applied Computing
March 21 - 24, 2011
TaiChung, Taiwan

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all
  • (2024)A Survey on the Applications of Semi-supervised Learning to Cyber-securityACM Computing Surveys10.1145/365764756:10(1-41)Online publication date: 22-Jun-2024
  • (2024)Exploring Algorithmic Paradigms in Message Classification: Insights from the Enron E-mail DatasetAdvances in Information Communication Technology and Computing10.1007/978-981-97-6103-6_3(27-40)Online publication date: 2-Oct-2024
  • (2023)Email spam detection using hierarchical attention hybrid deep learning methodExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120977233:COnline publication date: 15-Dec-2023
  • (2021)Advances in spam detection for email spam, web spam, social network spam, and review spamJournal of Computer Security10.3233/JCS-21002229:5(473-529)Online publication date: 26-Aug-2021
  • (2021)An empirical study of supervised email classification in Internet of Things: Practical performance and key influencing factorsInternational Journal of Intelligent Systems10.1002/int.22625Online publication date: 17-Aug-2021
  • (2015)An empirical study on email classification using supervised machine learning in real environments2015 IEEE International Conference on Communications (ICC)10.1109/ICC.2015.7249515(7438-7443)Online publication date: Jun-2015
  • (2014)Towards Designing an Email Classification System Using Multi-view Based Semi-supervised LearningProceedings of the 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications10.1109/TrustCom.2014.26(174-181)Online publication date: 24-Sep-2014

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