Forecasting turning trends in knowledge networks

Ádám Szántó-Várnagy, I. Farkas

Research output: Contribution to journalArticle

Abstract

A large portion of our collective human knowledge is in electronic repositories. These repositories range from “hard fact” databases (e.g., scientific publications and patents) to “soft” knowledge such as news portals. The common denominator between them all is that they can be thought of in terms of topics/keywords. The interest in these topics is constantly changing over time. Their frequency occurrence diagrams can be used for effective prediction by the most straightforward simplification. In this paper, we use these diagrams to produce simple and human-readable rules that are able to predict the future trends of the most important keywords in 5 data sets of different types. A thorough analysis of the necessary input variables and parameters and their relation to the success rate is presented, as well.

Original languageEnglish
Pages (from-to)110-122
Number of pages13
JournalPhysica A: Statistical Mechanics and its Applications
Volume507
DOIs
Publication statusPublished - Oct 1 2018

Keywords

  • Prediction
  • Time dynamics
  • Topic evolution
  • Topic similarities

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics

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