By representing a text by a set of words and their co-occurrences, one obtains a word-adjacency network -a network being in a way a reduced representation of the given language sample. In this paper, the possibility of using network representation in order to extract information about individual language styles of literary texts is studied. By determining selected quantitative characteristics of the networks and applying machine learning algorithms, it is made possible to distinguish between texts of different authors. It turns out that within the studied set of texts in English and Polish, the properly rescaled weighted clustering coefficients and weighted degrees of only a few nodes in the word-adjacency networks are sufficient to obtain the accuracy of authorship attribution over 90%. A correspondence between the authorship of texts and the structure of word-adjacency networks can therefore clearly be found; it may be stated that the network representation allows to distinguish individual language styles by comparing the way the authors use particular words and punctuation marks. The presented approach can be viewed as a generalization of the authorship attribution methods based on simplest lexical features. Apart from the characteristics given above, other network parameters are studied, both local and global ones, for both the unweighted and weighted networks. Their potential to capture the diversity of writing styles is discussed; some differences between languages are also observed. complex systems exhibit emergent properties, which are characteristic for a system as a whole, and cannot be simply derived or predicted solely from the properties of the system's individual elements. The existence of the emergent phenomena is often summarized with the phrases such as "more is different" [28] or "the whole is something besides the parts" [29] . In this context, natural language is clearly a vivid instance of a complex system. Higher levels of its structure usually cannot be simply reduced to the sum of the elements involved. For example, phonemes or letters basically do not have any meaning, but the words consisting of them are references to specific objects and concepts. Likewise, knowing the meaning of separate words does not necessarily provide the understanding of a sentence composed of them, as a sentence can carry additional information, like an emotional load or a metaphorical message. Other features of natural language that are typical for complex systems have also been studied, for example long-range correlations [30, 31] , fractal and multifractal properties [32] [33] [34] [35] [36] , self-organization [37] [38] [39] or the lack of characteristic scale, which manifests itself in power laws such as the well-known Zipf's law [40, 41] or Heaps' law (the latter also referred to as the Herdan's law [42] [43] [44] [45] ). The network formalism has proven to be useful in studying and processing natural language. It allows to represent language on many levels of its structure -the linguistic networks can be constructed to reflect word co-occurrence, semantic similarity, grammatical relationships etc. It has a significant practical importance -the methods of analysis of graphs and networks have been employed in the natural language processing tasks, like keyword selection, document summarization, word-sense disambiguation or machine translation [46] [47] [48] [49] [50] . Networks also seem to be a promising tool in research on the language itself [51]; they have been applied to study the language complexity [52-55], the structural diversity of different languages [56] , the role of punctuation in written language [57], or the organization of texts into sentences [58, 59] . Furthermore, the network formalism appears at the interface between linguistics and other scientific fields, for example in sociolinguistics, which, by studying social networks, investigates human language usage and evolution [60] [61] [62] . In this paper the properties of the word-adjacency networks constructed from literary texts are studied. Such networks may be treated as a representation of a given text, taking into account mutual co-occurrences of words. They can be characterized by some parameters that describe their structure quantitatively. Determining these parameters results in obtaining a set of a few numbers capturing considered features of networks and, consequently, features of the underlying text sample. In that sense the procedure of constructing a network from a text and calculating its characteristics may be viewed as extracting essential traits of the language sample and putting them into a compact form. Subjecting obtained data to data mining algorithms reveals that the networks representing texts of different authors can be distinguished one from another by their parameters. After appropriately proposed preparation of data, such procedure of authorship attribution, though quite simple, results in relatively high accuracy. It therefore leads to a statement that information about individual language style used in a given text can be drawn from the set of characteristics describing the corresponding word-adjacency network. This is comparable with the known results concerning identification of individual language styles using the linguistic networks [63-67]; however, the previous research has typically focused on the network types and the network structure aspects other than considered here. Data and methods The set of studied texts consists of novels in English and Polish. For each language, there are 48 novels, 6 novels for each of 8 different authors. The full list of considered books and authors is presented in Appendix. The texts were downloaded from the websites of Project Gutenberg [68], Wikisources [69], and Wolne Lektury [70] . Each text studied in this paper is transformed into a word-adjacency network, and resulting networks are subjected to adequate calculations and analyses. However, before the construction of the networks, the texts are appropriately pre-processed. Preparation includes: removing annotations and redundant whitespaces, converting all characters to lowercase, and replacing some types of punctuation marks by special symbols,
In REF , the authors used a co-occurrence network to study a corpus of English and Polish books.
Linguistic data mining with complex networks: a stylometric-oriented approach
Information Sciences 482, 301-320 (2019)
Mathematics Computer Science Physics