Social network models for enhancing reference-based search engine rankings

N. Korfiatis, M.-A. Sicilia, C. Hess, K. Stein, C. Schlieder

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)


In this chapter we discuss the integration of information retrieval information from two sources-a social network and a document reference network-for enhancing reference-based search engine rankings. In particular, current models of information retrieval are blind to the social context that surrounds information resources, thus they do not consider the trustworthiness of their authors when they present the query results to the users. Following this point we elaborate on the basic intuitions that highlight the contribution of the social context-as can be mined from social network positions for instance-into the improvement of the rankings provided in reference-based search engines. A review on ranking models in Web search engine retrieval along with social network metrics of importance such as prestige and centrality are provided as background. Then a presentation of recent research models that utilize both contexts is provided, along with a case study in the Internet-based encyclopedia Wikipedia, based on the social network metrics.
Original languageEnglish
Title of host publicationSocial Information Retrieval Systems
Subtitle of host publicationEmerging Technologies and Applications for Searching the Web Effectively
EditorsDion Goh, Schubert Foo
Number of pages25
ISBN (Electronic)9781599045450
Publication statusPublished - Oct 2007

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