article

Link mining: a survey

Authors:

Christopher P. DiehlAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 7, Issue 2

Pages 3 - 12

https://doi.org/10.1145/1117454.1117456

Published: 01 December 2005 Publication History

Abstract

Many datasets of interest today are best described as a linked collection of interrelated objects. These may represent homogeneous networks, in which there is a single-object type and link type, or richer, heterogeneous networks, in which there may be multiple object and link types (and possibly other semantic information). Examples of homogeneous networks include single mode social networks, such as people connected by friendship links, or the WWW, a collection of linked web pages. Examples of heterogeneous networks include those in medical domains describing patients, diseases, treatments and contacts, or in bibliographic domains describing publications, authors, and venues. Link mining refers to data mining techniques that explicitly consider these links when building predictive or descriptive models of the linked data. Commonly addressed link mining tasks include object ranking, group detection, collective classification, link prediction and subgraph discovery. While network analysis has been studied in depth in particular areas such as social network analysis, hypertext mining, and web analysis, only recently has there been a cross-fertilization of ideas among these different communities. This is an exciting, rapidly expanding area. In this article, we review some of the common emerging themes.

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cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 7, Issue 2

December 2005

152 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/1117454

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Copyright © 2005 Authors.

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Association for Computing Machinery

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Published: 01 December 2005

Published in SIGKDD Volume 7, Issue 2

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