Node similarity in networked information spaces

Abstract

Netvorked information spaces contain information entities, corresponding to nodes, vhich are (:orlrle(:l.ed [)y associm.i(ms, (:or'r'esporldirlg 1.o links irl (.he nel.wor'k. Exarrq)les of nel.wor'ked information spaces are: the World Wide Web, vhere information entities are veb pages, and associations are hyperlinks; the scientific literature, vhere information entities are articles and associations are references to other articles. SimilariW betveen information entities in a net- vorked information space can be defined not only based on the content of the information entities, but also based on the connectivity established by the associations present. This paper explores the definition of similariW based on connectivity only, and proposes several algorithms [)r' I, his [mr'pose. Our' rrlei, r'i(:s I,ake mJvard,age o[' I, he local rleigh[)or'hoo(ts o[' I, he rmcJes irl I, he rlel,- is no required, as long as a query engine is available for fo]]oving ]inks and extracting he necessary local neighbourhoods for similarity estimation. Tvo variations of similarity estimation beveen vo nodes are described, one based on he separate local neighbourhoods of he nodes, and another based on he join local neighbourhood expanded from boh nodes a he same ime. The algorithms are imp]emened and evaluated on he citation graph of computer science. The immediate application of his vork is in finding papers similar o a given paper [he Web.