Effects of missing data in multilayer networks
2016 (English)In: SOCIAL NETWORK ANALYSIS AND MINING, ISSN 1869-5450, Vol. 6, no 1Article in journal (Refereed) Published
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. 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 six real and eleven synthetic 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.
Place, publisher, year, edition, pages
2016. Vol. 6, no 1
Missing data, Multilayer networks, Social network analysis
IdentifiersURN: urn:nbn:se:uu:diva-304132DOI: 10.1007/s13278-016-0384-3ISI: 000382364400012OAI: oai:DiVA.org:uu-304132DiVA: diva2:1023283