Measuring originality in knowledge networks

Ádám Szántó-Várangy, Péter Pollner, Illés J. Farkas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Human knowledge is accumulated in several ways: through patents, scientific publications, encyclopedias, news, etc. In each case the involved “knowledge items” form a directed network that shows which item is built on which others. For example, patents (nodes) cite (link to) other patents (nodes). The usefulness of knowledge is most often measured on single knowledge items by article-level metrics (ALMs). In science the most common ALM is the citation number, n, quantifying impact. Instead of the impact here we discuss originality. We compute the probability, p, of directed links pointing from a node’s in-neighbors to its out-neighbors. Low values of p mean high originality. For several large real knowledge networks we find a very low correlation between n and p. Thus, we suggest that p provides qualitatively novel information about single knowledge items of human knowledge, such as patents, scientific publications, encyclopedia and news articles, etc.

Original languageEnglish
Title of host publicationComputational Social Networks - 4th International Conference, CSoNet 2015, Proceedings
EditorsNam P. Nguyen, Huawei Shen, My T. Thai
PublisherSpringer Verlag
ISBN (Print)9783319217857
Publication statusPublished - Jan 1 2015
Event4th International Conference on Computational Social Networks, CSoNet 2015 - Beijing, China
Duration: Aug 4 2015Aug 6 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9197
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Computational Social Networks, CSoNet 2015
CountryChina
CityBeijing
Period8/4/158/6/15

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Szántó-Várangy, Á., Pollner, P., & Farkas, I. J. (2015). Measuring originality in knowledge networks. In N. P. Nguyen, H. Shen, & M. T. Thai (Eds.), Computational Social Networks - 4th International Conference, CSoNet 2015, Proceedings (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9197). Springer Verlag.