Dynamic burstiness of word-occurrence and network modularity in textbook systems

Xue Mei Cui, Chang No Yoon, Hyejin Youn, Sang Hoon Lee, Jean S. Jung, Seung Kee Han*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We show that the dynamic burstiness of word occurrence in textbook systems is attributed to the modularity of the word association networks. At first, a measure of dynamic burstiness is introduced to quantify burstiness of word occurrence in a textbook. The advantage of this measure is that the dynamic burstiness is decomposable into two contributions: one coming from the inter-event variance and the other from the memory effects. Comparing network structures of physics textbook systems with those of surrogate random textbooks without the memory or variance effects are absent, we show that the network modularity increases systematically with the dynamic burstiness. The intra-connectivity of individual word representing the strength of a tie with which a node is bound to a module accordingly increases with the dynamic burstiness, suggesting individual words with high burstiness are strongly bound to one module. Based on the frequency and dynamic burstiness, physics terminology is classified into four categories: fundamental words, topical words, special words, and common words. In addition, we test the correlation between the dynamic burstiness of word occurrence and network modularity using a two-state model of burst generation.

Original languageEnglish (US)
Pages (from-to)103-110
Number of pages8
JournalPhysica A: Statistical Mechanics and its Applications
StatePublished - Dec 1 2017


  • Learning dynamics
  • Network community structure
  • Physics knowledge structure
  • Physics terminology network
  • Temporal analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics


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