This thesis bridges between two scientific fields -- linguistics and computer science -- in terms of Linguistic Networks. From the linguistic point of view we examine whether languages can be distinguished when looking at network topology of different linguistic networks. We deal with up to 17 languages and ask how far the methods of network theory reveal the peculiarities of single languages. We present and apply network models from different levels of linguistic representation: syntactic, phonological and morphological. The network models presented here allow to integrate various linguistic features at once, which enables a more abstract, holistic view at the particular language.

From the point of view of computer science we elaborate the instrumentarium of network theory applying it to a new field. We study the expressiveness of different network features and their ability to characterize language structure. We evaluate the interplay of these features and their goodness in the task of classifying languages genealogically. Among others we compare network features related to: average degree, average geodesic distance, clustering, entropy-based indices, assortativity, centrality, compactness etc. We also propose some new indices that can serve as additional characteristics of networks. The results obtained show that network models succeed in classifying related languages, and allow to study language structure in general. The mathematical analysis of the particular network indices brings new insights into the nature of these indices and their potential when applied to different networks.