Network Differences between Normal and Shuffled Texts: Case of Croatian

Comparison of the language networks from literature and blogs

“…The node selectivity measure can capture the effective distribution of numbers in the weighted adjacency matrix, and it is obtained as a ratio of (out/in-) node strength and its (out/in-) degree: (9) All presented measures are standard network measures usually used for network structure analysis, except the node selectivity measure which is introduced in [10] as the measure that can differentiate between networks based on normal and randomized texts. According to these results, we expected that node selectivity may be potentially important for the text categories differentiation and include it in the set of standard network measures.…”

“…Various types of linguistic networks have already been studied: syntax networks [1,2], semantic networks [3], phonological networks [4], syllable networks [5,6], word co-occurrence networks [7][8][9][10][11][12][13][14][15][16][17][18][19]. In [3,20,21] a systematic methodological overview of linguistic complex networks principles is presented.…”

“…In [3,20,21] a systematic methodological overview of linguistic complex networks principles is presented. Recently, linguistic co-occurrence networks have been intensively studied in order to analyse the structure of the language [7][8][9][10][11][12][13][14][15][16][17][18][19].…”

“…In our previous research [8,9] we showed the advantages of using a directed and weighted cooccurrence network as the model to capture the structure of a text. In this work we study global and local network measures for the networks constructed from different categories of texts.…”

Network Motifs Analysis of Croatian Literature

“…These features position Croatian among morphologically rich and free word-order languages. So far Croatian has been quantified in a complex networks framework based on the word co-occurrences [7], [1] and compared with shuffled counterparts [8], [9].…”