TY - JOUR
T1 - Dynamic burstiness of word-occurrence and network modularity in textbook systems
AU - Cui, Xue Mei
AU - Yoon, Chang No
AU - Youn, Hyejin
AU - Lee, Sang Hoon
AU - Jung, Jean S.
AU - Han, Seung Kee
N1 - Funding Information:
S.K.H. was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1A02019371). X.M.C. was supported by Jilin Education Science 12th Five-Year project of China under Grant No. GH14018. H.Y. acknowledges the support of the National Science Foundation (no. SMA-1312294). The authors appreciate discussions with Prof. H. Jeong and Dr. W. S. Kim.
Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - 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.
AB - 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.
KW - Learning dynamics
KW - Network community structure
KW - Physics knowledge structure
KW - Physics terminology network
KW - Temporal analysis
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U2 - 10.1016/j.physa.2017.06.002
DO - 10.1016/j.physa.2017.06.002
M3 - Article
AN - SCOPUS:85022027497
SN - 0378-4371
VL - 487
SP - 103
EP - 110
JO - Physica A: Statistical Mechanics and its Applications
JF - Physica A: Statistical Mechanics and its Applications
ER -