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Showing papers on "Katz centrality published in 2000"


Journal ArticleDOI
TL;DR: A new formalization of a “node-centrality” is provided which leads to some properties a measure of centrality has to satisfy, which allow to test given measures, for example measures based on degree, closeness, betweenness or Bonacich’s eigenvector- centrality.

393 citations


Journal ArticleDOI
TL;DR: New measures of centrality that summarize the contact structure of social networks are proposed that consider all the possible paths, do not require intensive computer calculations, and can be used to compare networks of different sizes because they are independent of the size of the networks.

96 citations



Dissertation
01 Jan 2000
TL;DR: In this paper, the authors examine the relative contributions of feature centrality and feature variability in property induction, whether centrality offers a domain-general or a domain specific constraint, and whether the centrality can operate under conditions of vagueness.
Abstract: This thesis examines property generalization among concepts. Its primary objective is to investigate the hypothesis that the more central a feature for a concept, the higher its generalizability to other concepts that share a similar structure (features and dependencies). Its secondary objectives are to examine the relative contributions of feature centrality and feature variability in property induction, whether centrality offers a domain-general or a domain-specific constraint, and whether centrality can operate under conditions of vagueness. Experiments 1 and 2 addressed the centrality hypothesis with centrality measured, whereas Experiments 3 to 14 and 17 with centrality manipulated. Relative feature centrality was manipulated as follows: from a single-dependency chain (Experiments 3 to 7), from the number of properties that depended upon a feature (Experiments 8 to 11 and 17), and from the centrality of the properties that depended upon the critical features (Experiments 12 to 14). The results support the centrality hypothesis. Experiments 12 to 16 addressed the relative contributions of centrality and variability in property induction. Experiments 12 to 14 pitted a central and variable property against a less central and less variable property in judgments of frequency and inductive strength. The results suggest that property induction depends on centrality rather than frequency information, and that centrality can bias the perception of frequency (although the latter results were not clear-cut). Experiments 15 and 16 pitted centrality against variability in information seeking. The results show that centrality information is sought more often than variability information to make an inference, especially amongst dissimilar concepts. Experiments 1 to 16 used animal categories. Experiment 17 examined the centrality hypothesis with artifact categories. The results show centrality effects. Taken together, the Experiments suggest that centrality offers a domain-general constraint. Experiments 5, 8 to 11, and 17 left the properties that depended upon a candidate feature unspecified. A centrality effect was still obtained. The results suggest that centrality can operate under conditions of vagueness. The results are discussed in terms of theories of conceptual structure and models of category-based inference. A model to capture the present findings is also sketched.

2 citations