The history of graph (network) theory (GNT) started with an attempt to find a single walking path, which crosses, once and only once, each of the seven bridges of old Königsberg; this is known as the Seven Bridges of Königsberg Problem. Since 1736, when Leonhard Euler proved the problem to be unsolvable using a very simple graph, GNT was developed, and it rapidly come to be used in a number of fields. Nowadays, GNT is actively used in a wide variety of disciplines from mathematics and physics to sociology and linguistics (e.g., Mehler, A., et al, 2016), as our world is full of systems, which can be represented and analyzed as networks.
The main focus of this paper is a presentation of a network visualization and analysis, based on an association network constructed on Japanese temporal and spatial lexical items. The network (Fig.1) is based on the results of an ongoing free word association experiment, the first stage of which was conducted in Tokyo in 2015, involving 85 native Japanese speaking participants of two different age groups (one in their 20s and one from their 50s to 70s).
Particular temporal and spatial lexical items for the experiment were selected on the basis of four main sources: A Frequency Dictionary of Japanese (2013), Japanese Word Association Database ver. 1 (2004), Associative Concept Dictionary (2004, 2005) and Japanese WordNet ver. 1.1. The criteria for the selection were based on a variety of frequencies according to Frequency Dictionary of Japanese (from Toki with 2514 occurrences per million words to Ima with 9 occurrences per million words) and a variety of semantic relations within the stimuli set (synonyms, hyponyms, antonyms). Synonyms (partial synonyms) are represented by kuukan, supeesu, yochi, hirogari; basho, ba; sukima, suki; ima, ribingu; aida, ma; jikan, toki, taimingu； kyuujitsu, yasumi, hima; basho, ba; wagaya, mai hoomu; nagasa, kyori; hizuke, hi. Synonyms are chosen in accordance with WordNet.
The hyponyms and hypernyms in this study are heya, apaato, manshon/ie; aki, natsu/ kisetsu; jidai, jiki, naganen, kisetsu, shunkan, hi/jikan; asa, yoru, hiruma/hi, oku, ie /kuukan； mukashi/toki. Hyponyms and hypernyms are selected in accordance with the Japanese Word Association Database and the Associative Concept Dictionary.
Also, soto, uchi; mae, ushiro; kako, mirai; tonai, kougai were selected as antonyms or opposites in accordance with the Japanese Word Association Database and the Associative Concept Dictionary.
Ten fillers were chosen randomly with the criterion to cover approximately the same frequency range as within the stimuli set. The fillers were added to the survey to serve as distraction from temporal and spatial stimuli words and to minimize the number of deliberate responses; the responses to the fillers are not included in the analysis.
The main purposes of this study are on three different levels: first, a macro-level, discussing the possibility of utilizing the association network analysis to describe the conceptual structure of the language in question; second, a meso-level, analyzing communities formed within the network; and third, a micro-level, investigating the usage of association networks to formulate the cognitive definitions of single words within the network by identifying their features based on their connections within the network.
At this stage of analysis, the findings suggest that the analysis of single word connections and their weight might be utilized for disambiguation of meanings of synonymic words for cognitive definitions (Ostermann, C., 2015). It demonstrates information which could be also found in traditional dictionary definitions or corpora materials, such as typical syntagmatic connections, e.g., sukima-kaze and suki-yudan. At the same time, culturally specific semantic features of the lexical items, which can hardly be predicted through the materials based on the common language production, e.g., ushiro-kowai or both negative and positive emotional evaluation of hima, can be found.
At the meso-level, ten communities, e.g., abstract space, concrete (physical) space, life time, dark/light time, home, etc., were detected within the network using the Order Statistics Local Optimization Method. The structure of connections between the communities is complex with numerous overlaps. However, on the basis of the inter-communities connections, it is still possible to hypothesize about a macro-level conceptual structure of Japanese, e.g. based on this analysis, it could be concluded that temporal and spatial concepts in modern Japanese are the most closely connected to two concepts: emotional evaluation and daily life (Fig. 2).
Finally, on the basis of this analysis, I propose an associative network as an illustrative and effective tool for planning further experimental work.
 Caldarelli, G. (2007). Scale-free networks: Complex webs in nature and technology. Oxford: Oxford: Oxford University Press.
 Dorogovtsev, S. N. (2010). Lectures on complex networks. Oxford: Oxford: Oxford University Press.
 Japanese Wordnet (v1.1), copyright NICT, 2009-2010
 Joyce, T. Large-scale Database of Japanese Word Associations, Version1, http://www.valdes.titech.ac.jp/~terry/jwad.html
 Lancichinetti, A., Radicchi, F., Ramasco, J.J., Fortunato S. (2011). Finding statistically significant communities in networks. PLoS ONE 6: e18961.
 Mehler, A., Lücking, A., Banisch, S., Blanchard, P., & Job, B. (Eds.). (2016). Towards a theoretical framework for analyzing complex linguistic networks. Berlin: Springer Berlin Heidelberg.
 Newman, M. E. J. (2010). Networks: An introduction. Oxford: Oxford: Oxford University Press.
 Okamoto, J., Ishizaki, S. (2004, 2005) Rensoogainenjisho. Associative Concept Dictionary
 Ostermann, C. (2015) Cognitive Lexicography. A New Approach to Lexicography Making Use of Cognitive Semantics, Berlin, Boston: De Gruyter Mouton
 Tono, Y., Yamazaki M., Maekawa K. (Eds.). (2013). A frequency dictionary of Japanese: Core vocabulary for learners. London : Routledge