Investigating the types and effects of missing data in multilayer networks

A common problem in social network analysis is the presence of missing data. This problem has been extensively investigated in single layer networks, that is, considering one network at a time. However, in multilayer networks, in which a holistic view of multiple networks is taken, the problem has not been specifically studied, and results for single layer networks are reused with no adaptation. In this work, we take an exhaustive and systematic approach to understand the effect of missing data in multilayer networks. Differently from the single layer networks, depending on layer interdependencies, the common network properties can increase or decrease with respect to the properties of the complete network. Another important aspect we observed through our experiments on real datasets is that multilayer network properties like layer correlation and relevance can be used to understand the impact of missing data compared to measuring traditional network measures.