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Web graph similarity for anomaly detection

Journal of Internet Services and Applications volume 1, pages 19–30 (2010)Cite this article

Abstract

Web graphs are approximate snapshots of the web, created by search engines. They are essential to monitor the evolution of the web and to compute global properties like PageRank values of web pages. Their continuous monitoring requires a notion of graph similarity to help measure the amount and significance of changes in the evolving web. As a result, these measurements provide means to validate how well search engines acquire content from the web. In this paper, we propose five similarity schemes: three of them we adapted from existing graph similarity measures, and two we adapted from well-known document and vector similarity methods (namely, the shingling method and random projection based method). We empirically evaluate and compare all five schemes using a sequence of web graphs from Yahoo!, and study if the schemes can identify anomalies that may occur due to hardware or other problems.

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Authors and Affiliations

  1. Stanford University, Stanford, CA, 94305, USA

    Panagiotis Papadimitriou & Hector Garcia-Molina

  2. Yahoo! Inc., Sunnyvale, CA, 94089, USA

    Ali Dasdan

Authors
  1. Panagiotis Papadimitriou
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  2. Ali Dasdan
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  3. Hector Garcia-Molina
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Correspondence to Panagiotis Papadimitriou.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Papadimitriou, P., Dasdan, A. & Garcia-Molina, H. Web graph similarity for anomaly detection. J Internet Serv Appl 1, 19–30 (2010). https://doi.org/10.1007/s13174-010-0003-x

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  • DOI: https://doi.org/10.1007/s13174-010-0003-x

Keywords

  • Anomaly detection
  • Graph similarity
  • Locality sensitive hashing