## 2 Related Works  
### What Are Complexity and Interpretability in PM?  
In different system modeling domains, research and development are mainly aimed at a high accuracy of model fit, i.e., capturing dependencies in data, while interpretation receives little attention. It is believed that interpretation can increase model trust, which is particularly acute for black-box models, even though it may affect predictive accuracy. This sparked a new line in machine learning [8] and artificial intelligence [9], in which models can already learn complicated data patterns and make precise forecasts. However, there is no strict definition of interpretability. When it comes to the domain of PM, the interpretability of the process model is usually considered as human interpretation and understanding [10] or as semantic mapping of process models [11]. In both cases, the interpretation refers to domain knowledge containing implicitly in the operators' expertise or explicitly in semantic knowledge bases (furtherly mentioned as \"domain interpretation\").  
When it comes to system insight, one can also reason about its complexity. Like interpretability, complexity is context-sensitive and can hardly be defined universally. There are many definitions and meanings of complex systems and complexity measures. The latter can be divided into two major classes: computational and system-related complexity measures [12]. Interpretability and complexity are closely related: a more straightforward description produced by the system tends to be better construed. On the other hand, higher complexity leads to lower interpretability. Such contradiction is observed in multiple research studying various measures in PM [10; 13; 14].  
The comprehensive nature of a human or of a business process model can be influenced by many factors, ranging from personal characteristics to elements of Gestalt theory [15]. Such factors include process modelling notation and its features (visual expressiveness, semantic transparency, etc.), model size (number of elements, diameter), modularity and structuredness (use of constructions with split/join usually improves model understanding), decomposition (ways to hide unimportant information from the user, thereby improving the quality of the model), etc. Within PM studies, [PERSON] et al. [10] investigated aspects that may influence process model comprehension. The authors used questionnaires with several process models that were filled by students at three universities. The students were taking or had completed classes on PM and had different levels of subject knowledge. The study revealed that in addition to personal factors, model size is correlated to its comprehension. A similar result was obtained in another empirical study [16]: the larger the distance of a process structure tree, the more challenging the process behavior to perceive it. Statistical analysis also indicated that cognitive difficulty can be ranked when understanding different relationships between model elements. In [17], process model complexity was defined as the degree to which a process is difficult to analyze, understand, or explain. Model structure, therefore, can serve as strong evidence of its complexity. In PM, the complexity term is often associated with logical blocks, such as AND-, XOR-, OR-splits, and loops presented in the model. These split constructs can be evaluated and quantified using different measures, e.g., control-flow complexity [17], entropy-based uncertainty [18], and others [19]. Their sum is the overall architectural complexity or uncertainty. Other measures of complexity can be derived if one considers process models from the perspective of neighboring disciplines [20]. In this study, we look at a process model as a graph structure and exploit several network complexity measures to find an understandable and interpretable model.