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Cited by 166

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Who links to whom: mining linkage between Web sites

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Bharat, K., Bay-Wei Chang, Henzinger, M., Ruhl, M.·2002·Proceedings 2001 IEEE International Conference on Data Mining

Abstract

Previous studies of the Web graph structure have focused on the graph structure at the level of individual pages. In actuality the Web is a hierarchically nested graph, with domains, hosts and Web sites introducing intermediate levels of affiliation and administrative control. To better understand the growth of the Web we need to understand its macro-structure, in terms of the linkage between Web sites. We approximate this by studying the graph of the linkage between hosts on the Web. This was done based on snapshots of the Web taken by Google in Oct 1999, Aug 2000 and Jun 2001. The connectivity between hosts is represented by a directed graph, with hosts as nodes and weighted edges representing the count of hyperlinks between pages on the corresponding hosts. We demonstrate how such a "hostgraph" can be used to study connectivity properties of hosts and domains over time, and discuss a modified "copy model" to explain observed link weight distributions as a function of subgraph size. We discuss changes in the Web over time in the size and connectivity of Web sites and country domains. We also describe a data mining application of the hostgraph: a related host finding algorithm which achieves a precision of 0.65 at rank 3.

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Research works printed but not linked: 19

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Metadata

Authors
Bharat, K., Chang, B.-W., Henzinger, M., & Ruhl, M.
Venue
Proceedings 2001 IEEE International Conference on Data Mining
Published
2002-11-14
Pages
51-58
ISSN
1550-4786 (ISSN-L)
OpenAlex
W2144982204
Cited by
166
References
0
FWCI
34.87
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Who links to whom: mining linkage between Web sites. https://app.cochat.ai/papers/10.1109/icdm.2001.989500
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Bharat, K., Chang, B.-W., Henzinger, M., & Ruhl, M. (2002). Who links to whom: mining linkage between Web sites. Proceedings 2001 IEEE International Conference on Data Mining. https://doi.org/10.1109/icdm.2001.989500

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