Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
DE
EN
Books
Journals
Events
Topic Page
Marketing
Log in
Learn more about individual access
Request company access
13th International Engine Congress 2026 | 24 + 25 February 2026

Meeting Place for the Powertrain and Sustainable Fuels Community

Take part as a speaker in the 13th International Engine Congress 2026, the Meeting Place for the Powertrain, and Sustainable Fuels Community for the exchange of experience. Submit a subject proposal online about our main subject areas: Passenger car engine technology, Commercial vehicle engine technology and Sustainable Fuels & Energy!
EXTENDED SEARCH
Log in
Top
Social Network Analysis and Mining
Published in:

01-12-2016 | Original Article

Effects of missing data in multilayer networks

Authors: Rajesh Sharma, Matteo Magnani, Danilo Montesi

Published in: Social Network Analysis and Mining | Issue 1/2016

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.
previous article Stepping back from the trees to see the forest: a network approach to valuing intelligence
next article Quality of life: a social network’s perspective

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Literature
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,
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 5:011027CrossRef
Dickson WJ, Roethlisberger FJ (1939) Management and the worker. Harvard University Press, Cambridge
Domenico MD, Solé-Ribalta A, Gómez S, Arenas A (2014) Navigability of interconnected networks under random failures. PNAS 111:8351–8356MathSciNetCrossRef
Gjoka M, Butts CT, Kurant M, Markopoulou A (2011) Multigraph sampling of online social networks. IEEE J Select Areas Commun 29(9):1893–1905CrossRef
Huisman M (2009) Imputation of missing network data: some simple procedures. J Soc Struct 10:1–29
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):247–268CrossRef
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-InterscienceMATH
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 257–264CrossRef
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 30:19–30CrossRef
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
Ward MD, Hoff PD, Lofdahl CL (2003) Identifying international networks: latent spaces and imputation. The National Academic Press, Washington, pp 345–360
Metadata
Title
Effects of missing data in multilayer networks
Authors
Rajesh Sharma
Matteo Magnani
Danilo Montesi
Publication date
01-12-2016
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2016
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-016-0384-3

Other articles of this Issue 1/2016

Unsupervised graph-based pattern extraction for multilingual emotion classification

  • Original Article

An empirical study of socialbot infiltration strategies in the Twitter social network

  • Original Article

Identifying and validating personality traits-based homophilies for an egocentric network

  • Original Article

Structure and causality in the world system

  • Original Article

Multi-source models for civil unrest forecasting

  • Original Article

An empirical comparison of Big Graph frameworks in the context of network analysis

  • Original Article

Premium Partner

    Image Credits