Dynamical Network Analysis of the South Korean Dialects Compared to Traditional Dialect Classification
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Dynamical Network Analysis of the South Korean Dialects Compared to Traditional Dialect Classification Seungsik Min
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Department of Natural Science, Korea Naval Academy, Changwon 51704, Korea (Received 4 March 2020; revised 12 May 2020; accepted 13 May 2020) In this paper, we investigate the network properties of the South Korean dialects. Among 153 words in “The Linguistic Atlas of Korea”, 33 words satisfying our criteria were surveyed. We set 138 basic administrative districts that make up South Korea as nodes and construct a language network by using a defined procedure. The network shows neither a small-world (small-world coefficient < 5) nor a scale-free (degree distribution forms like a unimodal Poisson) property in the general complex network. Also, the assortativity is seen to show transition from negative to positive at about the threshold value of 15. The number of Korean linguistic groups, which is the most import result in this study, is constantly four with s-curved Newman modularity in the threshold range 13 ∼ 22. The Korean dialect division by using a network modularity method is almost identical to the classification of traditional linguists (Central, Southwest, Southeast, and Jeju). Furthermore, subgroups can be obtained by performing a hierarchical dialect division. As a result, South Korean dialects are classified into seven minor groups, and some regions (e.g., Southern Gang-won) belong to groups different from those of the traditional classification method. Keywords: Korean dialect, Dialect division, Language atlas, Language network, Newman modularity DOI: 10.3938/jkps.77.303
I. INTRODUCTION Research on complex systems has been widely conducted in physics, biology, geology, IT, economics, social sciences, and so on [1–12]. Complex systems are formed by subunits that interact non-linearly with one another [3]. In other words, the system generates a synergy effect such that a physical quantity of the whole system is larger than the sum of the physical quantities of the sub-systems. Many interesting research objects are considered complex systems. In order to understand complex systems from holistic perspectives, we usually use a network or graph theory. Network theory is one of the great tools for explaining complex systems. As time series analysis represents longitudinal research, network analysis is often used for cross-sectional research of complex systems. Network theory has played a key role as a wonderful language to express a complex system since the 1960s [13]. Starting from the study of random network by Erd¨os and R´enyi (ER model) in 1959 [14], Watts and Strogatz (WS model) clarified the actor, C. elegans, and electrical power networks have small-world properties using clustering coefficient in 1998 [15]. Also, Barab´asi and Albert (BA model) investigated the scale-free properties of complex networks in 1999 [16]. Thereafter, research on com∗
plex networks has evolved explosively through the use of a variety of methods and data. Many studies have been done on the assortative property [17] and t
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