The first step in the analysis of an empirical multilayer network is to obtain and prepare the data. Although this may sound obvious, it is easy to underestimate the impact of the data collection and preprocessing phase in a data mining process, or the proportion of time spent on these activities and the subsequent interpretation of the results if compared with the time needed to execute the selected data mining algorithms. In fact, this is often the most time-consuming and crucial part of the process and has a great impact on the results of the analysis.

Even if it has become common for researchers in different areas to collect large amounts of digital data, with more or less reasonable assumptions of completeness (Morstatter et al., 2013), we must consider that, in most cases, not all the desired data are available or that the data can be too large to be processed with the available computational resources. In this case, sampling can be necessary, leading to an incomplete data set. However, these are only some of the reasons why our data can be inaccurate or incomplete. In Section 4.1, we discuss several sources of missing or inaccurate data inmultilayer social networks.

Then, after the data have been collected, they may not be ready to be analyzed. In a typical data mining process, a main part of data preprocessing consists in choosing the right features to represent the data, for example, descriptive variables like age or income, also called attributes. This may involve the selection of some specific values from raw data; the transformation of some of them, for example, expressing numerical attribute values in the range [0, 1]; or the combination of some attributes to generate new features. If we consider a multilayer social network, we can see each layer as a feature describing the actors in the model, very much like age or income, but focusing on the actors’ relationships instead of their personal characteristics.