## 1 Introduction  
The last few years have witnessed a considerable increase in interest in computer-generated music and automatic analysis of music pieces. The development of information technology renders it possible to increase the computational capabilities of the devices used, and thus to boost the aesthetic values of the composed pieces. The development of digital models of soundtracks additionally allows for automated analyzing of pieces, in particular extracting their features, discovering dependencies, and comparing tracks with each other. Nevertheless, methods of automatic music generation have existed for hundreds of years and predate computer science. The first compositions enabling the semi-automatic generation of pieces based on pre-composed samples date back to the second half of the 18 th century. The most renowned piece representing this style in music is the composition attributed to [PERSON] \"Musikalisches Wurfelspiel\" (\"Musical Dice Game\"), which allows the creation of almost 800 trillion sixteen-bar pieces, which was analyzed in [1]. As the authors of these pieces themselves note ([PERSON]--a piece entitled \"Einfall, einen doppelten Contrapunct in der Octave von sechs Tacten zu machen, ohne die Regeln davon zu wissen\"--\"A Method of Creating Six-Bar Pieces with DoubleCounterpoint in an Octave, Requiring no Knowledge of the Rules\"), music systems constructed in this way do not require knowledge of music theory, and this allows automated devices to compose pieces. Nowadays, there are other, much more complex methods of automatically generating music in use, employing advanced mathematical models and IT tools. One can mention methods utilizing neural networks: especially generative adversarial networks [2; 3], convolutional neural networks [4; 5], recurrent neural networks [6] and others (many of the remaining neural networks-based methods are described in [7]). These methods perform especially well when creating new arrangements or orchestrating existing soundtracks--it is worthwhile to note that the generated pieces are usually characterized by a good listenability. Other approaches include models based on genetic algorithms [8], score reduction [9], Bayesian networks (e.g., [10]), statistics (as proposed in [11]) and non-musical inspiration sources [12]. These models are specialized to serve other purposes such as chord prediction for a monophonic track, rhythm generation or harmonization of the musical piece. Most of the abovementioned methods use a given corpus of original pieces and create their arrangements (namely generate completely new soundtracks based on them) or harmonize a given soundtrack (namely create tracks of other instruments/voices \"matching\" the theme introduced by the user) while maintaining a style similar to their prototypes. What is worthwhile to note is the fact that some of these methods make it possible to generate polyphonic tracks characterized by high aesthetic values. On the other hand, all of these methods are generally focused on some aspects of automated music composition and therefore it is not possible to easily reuse them for other purposes such as comparing different musical tracks. One of such concepts that enables the creation of compositions based on original song tracks is the use of graph and network theory to represent the relationship between the notes used in the track. Analyzing the coexistence of notes and their cardinality helps draw conclusions about the original piece as well as generate arrangements containing similar musical themes (this procedure is thoroughly described in [13]). Contrary to the methods described above, the graph-based representation of music allows both to create new arrangements (i.e., a soundtrack similar to the piece represented by the graph) and to calculate the similarity of different pieces by comparing graph structures corresponding to them (and not by comparing the musical content itself).