Investigating the types and effects of missing data in multilayer networks
2015 (English)In: International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2015, 392-399 p.Conference paper (Refereed)
A common problem in social network analysis isthe presence of missing data. This problem has been extensivelyinvestigated in single layer networks, that is, considering onenetwork at a time. However, in multilayer networks, in which aholistic view of multiple networks is taken, the problem has notbeen specifically studied, and results for single layer networks arereused with no adaptation. In this work, we take an exhaustiveand systematic approach to understand the effect of missingdata in multilayer networks. Differently from the single layernetworks, depending on layer interdependencies, the commonnetwork properties can increase or decrease with respect to theproperties of the complete network. Another important aspectwe observed through our experiments on real datasets is thatmultilayer network properties like layer correlation and relevancecan be used to understand the impact of missing data comparedto measuring traditional network measures.
Place, publisher, year, edition, pages
2015. 392-399 p.
Missing Data, Multilayer Networks, Social Network Analysis
Computer and Information Science
IdentifiersURN: urn:nbn:se:uu:diva-266851ISI: 000371793500055ISBN: 9781450338547OAI: oai:DiVA.org:uu-266851DiVA: diva2:868904
International Conference on Advances in Social Networks Analysis and Mining (ASONAM,)Paris, France, August 25-28, 2015