The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of contexts on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics, through the discovery of “authoritative ” information sources on such topics. We propose and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of “hub pages ” that join them together in the link structure. Our formulation has connections to the eigenvectors of certain matrices associated with the link graph; these connections in turn motivate additional heuristics for link-based analysis.

The aggregated citation relations among journals included in the Science Citation Index provide us with a huge matrix which can be analyzed in various ways. Using principal component analysis or factor analysis, the factor scores can be used as indicators of the position of the cited journals in the citing dimensions of the database. Unrotated factor scores are exact, and the extraction of principal components can be made stepwise since the principal components are independent. Rotation may be needed for the designation, but in the rotated solution a model is assumed. This assumption can be legitimated on pragmatic or theoretical grounds. Since the resulting outcomes remain sensitive to the assumptions in the model, an unambiguous classification is no longer possible in this case. However, the factor-analytic solutions allow us to test classifications against the structures contained in the database. This will be demonstrated for the delineation of a set of biochemistry journals.

journals) are made accessible from the perspective of any of these journals. A vector-space model is used for normalization, and the results are brought online at

Introduction Becausethesciencesdevelopdynamically,oneexpects tofindchangeintrendlinesofscientometricindicators.For example,scientificproductivitychangesovertime,anditis alsoexpectedtodifferamongresearchgroups.Thevariationamongresearchgroupsateachmomentintimemay interactwiththeprocessesofchangeovertime.Apolicy analyst,therefore,maywishtoask"whatdotheresults teachus?"Shouldpoliciesnurturethe"weak"unitsor rather"pickthewinners"(Irvine&Martin,1984)?Doesa highscoreonanindicatorpredicateforfurthergrowthor ratherpredictrelativestabilityorevendecline?Inother words:whatisthestrategicvalueofthemeasurement resultsusingscientometricindicators?Howdotheindicated developmentsrelatetoabaselineforthecomparison? Thequestionoftheconstructionofabaselineforthe comparison(Studer&Chubin,1980)hasbeenprevailingin scientometricstudiesduringthe1980sand1990swithout havingbeensolvedhitherto.Twoimportantproposalsfor methodologiesweremaderightatthebeginningofthe scientometricresearchprogram,notably(a)tomakecompari

In this study, the authors have three objectives:

data using information theory