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.

[1]  Minas Gjoka,et al.  Multigraph Sampling of Online Social Networks , 2010, IEEE Journal on Selected Areas in Communications.

[2]  Matteo Magnani,et al.  Diffusion Processes on Multilayer Networks , 2014, ArXiv.

[3]  F. J. Roethlisberger,et al.  Management and the Worker , 1941 .

[4]  Gueorgi Kossinets.  Effects of missing data in social networks , 2006, Soc. Networks.

[5]  Mark Huisman,et al.  Imputation of missing network data: Some simple procedures , 2009, J. Soc. Struct..

[6]  Anna Monreale,et al.  On multidimensional network measures , 2013, SEBD.

[7]  Nicole A. Lazar,et al.  Statistical Analysis With Missing Data , 2003, Technometrics.

[8]  Jure Leskovec,et al.  The Network Completion Problem: Inferring Missing Nodes and Edges in Networks , 2011, SDM.

[9]  Christos Faloutsos,et al.  Sampling from large graphs , 2006, KDD '06.

[10]  Massimiliano Zanin,et al.  Emergence of network features from multiplexity , 2012, Scientific Reports.

[11]  Stanley Milgram,et al.  An Experimental Study of the Small World Problem , 1969 .

[12]  Matteo Magnani,et al.  Missing Data in Multiplex Networks: A Preliminary Study , 2014, 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems.

[13]  P. Bearman,et al.  Chains of Affection: The Structure of Adolescent Romantic and Sexual Networks1 , 2004, American Journal of Sociology.

[14]  Tshilidzi Marwala,et al.  Modeling of missing data prediction: Computational intelligence and optimization algorithms , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[15]  Minas Gjoka,et al.  Walking on a graph with a magnifying glass: stratified sampling via weighted random walks , 2011, PERV.

[16]  Jure Leskovec,et al.  Correcting for missing data in information cascades , 2011, WSDM '11.

[17]  Mason A. Porter,et al.  Multilayer Analysis and Visualization of Networks , 2014, J. Complex Networks.

[18]  Matteo Magnani,et al.  Formation of Multiple Networks , 2013, SBP.

[19]  Roderick J. A. Little,et al.  Statistical Analysis with Missing Data: Little/Statistical Analysis with Missing Data , 2002 .

[20]  Peter D. Hoff,et al.  Latent Space Approaches to Social Network Analysis , 2002 .

[21]  Enola K. Proctor,et al.  Imputing Missing Data: A Comparison of Methods for Social Work Researchers , 2006 .

[22]  Matteo Magnani,et al.  The ML-Model for Multi-layer Social Networks , 2011, 2011 International Conference on Advances in Social Networks Analysis and Mining.

[23]  Michael D. Ward,et al.  Identifying International Networks : Latent Spaces and Imputation , 2003 .

[24]  Muhammad Tayyab Asif,et al.  Low-dimensional models for missing data imputation in road networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[25]  W. Mackenzie,et al.  The Management and the Worker , 2008 .

[26]  Lynne E. Parker,et al.  A spatial-temporal imputation technique for classification with missing data in a wireless sensor network , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.