Book contents
- Frontmatter
- Dedication
- Contents
- list of abbreviations
- 1 Moving Out of Flatland
- PART I MODELS AND MEASURES
- PART II MINING MULTILAYER NETWORKS
- 4 Data Collection and Preprocessing
- 5 Visualizing Multilayer Networks
- 6 Community Detection
- 7 Edge Patterns
- PART III DYNAMICAL PROCESSES
- PART IV CONCLUSION
- Glossary
- Bibliography
- Index
4 - Data Collection and Preprocessing
from PART II - MINING MULTILAYER NETWORKS
Published online by Cambridge University Press: 05 July 2016
- Frontmatter
- Dedication
- Contents
- list of abbreviations
- 1 Moving Out of Flatland
- PART I MODELS AND MEASURES
- PART II MINING MULTILAYER NETWORKS
- 4 Data Collection and Preprocessing
- 5 Visualizing Multilayer Networks
- 6 Community Detection
- 7 Edge Patterns
- PART III DYNAMICAL PROCESSES
- PART IV CONCLUSION
- Glossary
- Bibliography
- Index
Summary
It is a Law of Nature with us that a male child shall have one more side than his father, so that each generation shall rise (as a rule) one step in the scale of development and nobility.
– The SquareThe 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.
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- Multilayer Social Networks , pp. 67 - 78Publisher: Cambridge University PressPrint publication year: 2016