Skip to main content
SpringerLink
Log in

Effects of missing data in multilayer networks

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Asif MT, Mitrovic N, Garg L, Dauwels J, Jaillet P (2013) Low-dimensional models for missing data imputation in road networks. In: IEEE international conference on acoustics, speech and signal processing, ICASSP 2013, Vancouver, BC, Canada, May 26–31, 2013, pp 3527–3531,

  • Barabasi A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512

    Article  MathSciNet  MATH  Google Scholar 

  • Cardillo A, Gómez-Gardeñes J, Zanin M, Romance M, Papo D, Pozo F, Boccaletti S (2013) Emergence of network features from multiplexity. Sci Rep 3

  • De Choudhury M, Lin Y.-R, Sundaram H, Candan K.S., Xie L, Kelliher A (2010) How Does the data sampling strategy impact the discovery of information diffusion in social media? In: Proceedings of the 4th international AAAI conference on weblogs and social media

  • De Domenico M, Porter MA, Arenas A (2014) Muxviz: a tool for multilayer analysis and visualization of networks. J Complex Netw

  • De Domenico M, Lancichinetti A, Rosvall M (2015) Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems. Phys Rev 5:011027

    Article  Google Scholar 

  • Dickson WJ, Roethlisberger FJ (1939) Management and the worker. Harvard University Press, Cambridge

    Google Scholar 

  • Domenico MD, Solé-Ribalta A, Gómez S, Arenas A (2014) Navigability of interconnected networks under random failures. PNAS 111:8351–8356

    Article  MathSciNet  Google Scholar 

  • Gjoka M, Butts CT, Kurant M, Markopoulou A (2011) Multigraph sampling of online social networks. IEEE J Select Areas Commun 29(9):1893–1905

    Article  Google Scholar 

  • Hoff PD, Raftery AE, Handcock MS (2002) Latent space approaches to social network analysis. J Am Stat Assoc 97:1090–1098

    Article  MathSciNet  MATH  Google Scholar 

  • Huisman M (2009) Imputation of missing network data: some simple procedures. J Soc Struct 10:1–29

    Google Scholar 

  • Kim M, Leskovec J (2011) The network completion problem: inferring missing nodes and edges in networks. In: Proceedings of the eleventh SIAM international conference on data mining, SDM 2011, April 28–30, 2011, Mesa, Arizona, USA, pp 47–58

  • Kossinets G (2006) Effects of missing data in social networks. Soc Netw 28(3):247–268

    Article  Google Scholar 

  • Leke C, Twala B, Marwala T (2014) Modeling of missing data prediction: Computational intelligence and optimization algorithms. In: IEEE international conference on systems, man and cybernetics (SMC), 2014, pp 1400–1404

  • Leskovec J, Faloutsos C (2006) Sampling from large graphs. In: KDD ’06: proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 631–636

  • Li Y, Parker LE (2008) A spatial-temporal imputation technique for classification with missing data in a wireless sensor network. In: Proceedings of IEEE international conference on intelligent robots and systems

  • Little RJA, Rubin DB (2002) Statistical analysis with missing data, 2nd edn. Hoboken, Wiley-Interscience

    MATH  Google Scholar 

  • Magnani M, Micenková B, Rossi L (2013) Combinatorial analysis of multiple networks. CoRR arXiv:1303.4986,

  • Magnani M, Monreale A, Rossetti G, Giannotti F (2013) On multidimensional network measures. In: Italian conference on Sistemi Evoluti per le Basi di Dati (SEBD)

  • Magnani M, Rossi L (2011) The ml-model for multi-layer social networks. In: International Conference on advances in social networks analysis and mining (ASONAM), 2011, pp 5–12

  • Magnani M, Rossi L (2013) Formation of multiple networks. Social computing., behavioral-cultural modeling and prediction. Springer, Berlin, pp 257–264

    Book  Google Scholar 

  • Mehdiabadi ME, Rabiee HR, Salehi M (2012) Sampling from diffusion networks. In: IEEE Computer Society, Social Informatics, pp 106–112

  • Mislove A, Marcon M, Gummadi KP, Druschel P, Bhattacharjee B (2007) Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM conference on internet measurement, IMC ’07, pp 29–42

  • Moody J, Stovel K, Bearman PS (2002) Chains of affection: the structure of adolescent romantic and sexual networks. ISERP Working Paper, Columbia University

  • Sadikov E, Medina M, Leskovec J, Garcia-Molina H (2011) Correcting for missing data in information cascades. In: Proceedings of the fourth ACM international conference on web search and data mining, WSDM ’11, New York, NY, USA, ACM, pp 55–64

  • Salehi M, Rabiee HR, Nabavi N, Pooya S (2011) Characterizing twitter with respondent-driven sampling. In: DASC, IEEE computer society, pp 1211–1217

  • Salehi M, Sharma R, Marzolla M, Montesi D, Siyari P, Magnani M (2014) Diffusion processes on multilayer networks. CoRR, arXiv:1405.4329,

  • Saunders JA, Morrow-Howell N, Spitznagel E, Dore P, Proctor EK, Pescarino R (2006) Imputing missing data: a comparison of methods for social work research. Soc Work Res 30:19–30

    Article  Google Scholar 

  • Sharma R, Magnani M, Montesi D (2014) Missing data in multiplex networks: a preliminary study. In: Third international workshop on complex networks and their applications

  • Sharma R, Magnani M, Montesi D (2015) Investigating the types and effects of missing data in multilayer networks. In: IEEE/ACM international conference on advances in social networks analysis and mining

  • Travers J, Milgram S (1967) An experimental study of the small world problem. Psychol Tod 2:60–67

    Google Scholar 

  • Ward MD, Hoff PD, Lofdahl CL (2003) Identifying international networks: latent spaces and imputation. The National Academic Press, Washington, pp 345–360

    Google Scholar 

Download references

Acknowledgments

This work has been supported in part by the Italian Ministry of Education, Universities and Research FIRB project RBFR107725.

Author information

Authors and Affiliations

  1. University of Bologna, Bologna, Italy

    Rajesh Sharma & Danilo Montesi

  2. Uppsala University, Uppsala, Sweden

    Matteo Magnani

Authors
  1. Rajesh Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matteo Magnani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Danilo Montesi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rajesh Sharma.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, R., Magnani, M. & Montesi, D. Effects of missing data in multilayer networks. Soc. Netw. Anal. Min. 6, 69 (2016). https://doi.org/10.1007/s13278-016-0384-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-016-0384-3

Keywords

Search

Navigation