Encyclopedia of Social Network Analysis and Mining

2018 Edition
| Editors: Reda Alhajj, Jon Rokne

Multiple Social Networks, Data Models and Measures for

  • Matteo Magnani
  • Luca Rossi
Reference work entry
DOI: https://doi.org/10.1007/978-1-4939-7131-2_33



A Social Network (SN)

is a set of social relationships between actors, where actors represent individuals, groups of individuals, or larger organizations. A social network can also contain nonrelational information, such as data about the individuals.

A Social Network Site (SNS)

is a Web 2.0 site where users can create user profiles and interact with other users, for example sharing messages.

An Online Social Network (OSN)

contains information collected from a SNS or from other online services about online interactions.


Multiple Social Network Analysis is a discipline defining models, measures, methodologies, and algorithms to study multiple social networks together as a single social system. It is particularly valuable when the networks are interconnected, e.g., the same actors are present in more than one network.


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  1. Afsarmanesh N, Magnani M (2016) Finding overlapping communities in multiplex networks. http://arxiv.org/abs/1602.03746
  2. Battiston F, Nicosia V, Latora V (2014) Structural measures for multiplex networks. Phys Rev E 89(3):032804.  https://doi.org/10.1103/PhysRevE.89.032804CrossRefGoogle Scholar
  3. Berlingerio M, Coscia M, Giannotti F (2011a) Finding and characterizing communities in multidimensional networks. In: International conference on advances in social networks analysis and mining (ASONAM), IEEE Computer Society Washington, DC, USA, pp. 490–494Google Scholar
  4. Berlingerio M, Coscia M, Giannotti F, Monreale A, Pedreschi D (2011b) Foundations of multidimensional network analysis. In: 2011 International conference on advances in social networks analysis and mining, IEEE Computer Society Washington, DC, USA, pp. 485–489.  https://doi.org/10.1109/ASONAM.2011.103
  5. Berlingerio M, Pinelli F, Calabrese F (2013) ABACUS: frequent pAttern mining-BAsed community discovery in mUltidimensional networkS. Data Min Knowl Disc 27(3):294–320MathSciNetzbMATHCrossRefGoogle Scholar
  6. Boyd D (2008) Taken out of context: American teen sociality in networked publics. PhD thesis, University of California-Berkeley, School of InformationGoogle Scholar
  7. Boyd D (2010) Privacy and publicity in the context of big data. WWW. Raleigh, North Carolina, April 29. http://www.danah.org/papers/talks/2010/WWW2010.html
  8. Bródka P, Stawiak P, Kazienko P (2011) Shortest path discovery in the multi-layered social network. In: 2011 International conference on advances in social networks analysis and mining, IEEE Computer Society Washington, DC, USA, pp. 497–501.  https://doi.org/10.1109/ASONAM.2011.67
  9. Bródka P, Kazienko P, Musial K, Skibicki K (2012) Analysis of neighbourhoods in multi-layered dynamic social networks. Int J Comp Intel Sys 5(3):582–596CrossRefGoogle Scholar
  10. Buldyrev SV, Parshani R, Paul G, Stanley HE, Havlin S (2010) Catastrophic cascade of failures in interdependent networks. Nature 464(7291):1025–1028.  https://doi.org/10.1038/nature08932CrossRefGoogle Scholar
  11. Cai D, Shao Z, He X, Yan X, Han J (2005a) Community mining from multi-relational networks. PKDD 3721:445–452. https://doi.org/10.1007/11564126 44CrossRefGoogle Scholar
  12. Cai D, Shao Z, He X, Yan X, Han J (2005b) Mining hidden community in heterogeneous social networks. In: International workshop on link discovery (LinkKDD), ACM Press, pp. 58–65.  https://doi.org/10.1145/1134271.1134280
  13. Celli F, Di Lascio FML, Magnani M, Pacelli B, Rossi L (2010) Social network data and practices: the case of friendfeed. In: International conference on social computing, behavioral modeling and prediction, Lecture notes in computer science. Springer, Berlin/HeidelbergGoogle Scholar
  14. Cheng X, Dale C, Liu J (2008) Statistics and social network of YouTube videos. In: 2008 16th International workshop on quality of service, IEEE Press Piscataway, NJ, USA, pp. 229–238.  https://doi.org/10.1109/IWQOS.2008.32
  15. Contractor N (2009) The emergence of multidimensional networks. J Comput-Mediat Commun 14(3):743–747.  https://doi.org/10.1111/j.1083-6101.2009.01465.xCrossRefGoogle Scholar
  16. Cozzo E, Kivelä M, De Domenico M, Solé A, Arenas A, Gómez S, Porter MA, Moreno Y (2015) Structure of triadic relations in multiplex networks. New J Phys 17, 073029Google Scholar
  17. De Domenico M, Nicosia V, Arenas A, Latora V (2015) Structural reducibility of multilayer networks. Nat Commun, 6:6864Google Scholar
  18. De Domenico M, Porter MA, Arenas A (2015) MuxViz: a tool for multilayer analysis and visualization of networks. J Complex Netw, 3(2): 159–176Google Scholar
  19. Dickison ME, Magnani M, Rossi L (2016) Multilayer social networks. Cambridge University PressGoogle Scholar
  20. Fatemi Z, Salehi M, Magnani M (2016) A simple multiforce layout for multiplex networks. http://arxiv.org/abs/1607.03914
  21. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3–5):75–174.  https://doi.org/10.1016/j.physrep.2009.11.002MathSciNetCrossRefGoogle Scholar
  22. Huang L, Liu J (2010) Characterizing multiplex social dynamics with autonomy oriented computing. In: Life system modeling and intelligent computing, Lecture Notes in Computer Science, vol 6329. Springer, Berlin, Heidelberg, pp 277–287Google Scholar
  23. Huberman BA, Romero DM, Wu F (2009) Social network that matter: twitter under the microscope. First Monday 14(1)Google Scholar
  24. Iacobucci D, Wasserman S (1990) Social networks with two sets of actors. Psychometrika 55(4):707–720CrossRefGoogle Scholar
  25. Kazienko P, Bródka P, Musial K, Gaworecki J (2010a) Multi-layered social network creation based on bibliographic data. In: Elmagarmid AK, Agrawal D (eds) SocialCom/PASSAT, IEEE Computer Society, pp 407–412Google Scholar
  26. Kazienko P, Bródka P, Musial K, Gaworecki J (2010b) Multi-layered social network creation based on bibliographic data. In: 2010 I.E. second international conference on social computing, IEEE, pp. 407–412.  https://doi.org/10.1109/SocialCom.2010.65
  27. Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA (2014) Multilayer networks. J Commun Netw 2(3):203–271.  https://doi.org/10.1093/comnet/cnu016CrossRefGoogle Scholar
  28. Lazega E, Pattison PE (1999) Multiplexity, generalized exchange and cooperation in organizations: a case study. Soc Networks 21(1):67–90CrossRefGoogle Scholar
  29. Lazega E, Jourda MT, Mounier L, Stofer R (2008) Catching up with big fish in the big pond? Multilevel network analysis through linked design. Soc Networks 30(2):159–176CrossRefGoogle Scholar
  30. Leskovec J, Huttenlocher D, Kleinberg J (2010) Predicting positive and negative links in online social networks. In: Proceedings of the 19th international conference on World wide web, ACM, New York, WWW’10, pp. 641–650.  https://doi.org/10.1145/1772690.1772756.
  31. Magnani M, Rossi L (2011a) The ML-model for multilayer network analysis. In: IEEE international conference on advances in social network analysis and mining, IEEE Computer Society, Los Alamitos.Google Scholar
  32. Magnani M, Rossi L (2011b) The ML-model for multi-layer social networks. In: International conference on social network analysis and mining (ASONAM), IEEE Computer Society Washington, DC, USA, pp. 5–12.Google Scholar
  33. Magnani M, Rossi L (2013a) Formation of multiple networks. In: Social computing, behavioral-cultural modeling and prediction. Springer, Berlin Heidelberg, pp 257–264CrossRefGoogle Scholar
  34. Magnani M, Rossi L (2013b) Pareto distance for multi-layer network analysis. In: Greenberg AM, Kennedy WG, Bos ND (eds) Social computing, behavioral-cultural modeling and prediction, Lecture notes in computer science, vol 7812. Springer, Berlin.  https://doi.org/10.1007/978-3-642-37210-0CrossRefGoogle Scholar
  35. Minor MJ (1983) New directions in multiplexity analysis. In: Burt RS, Minor MJ (eds) Applied network analysis. Sage, pp 223–244Google Scholar
  36. Moreno JL, Jennings HH (1934) Who shall survive?: a new approach to the problem of human interrelations. Nervous and Mental Disease Publishing Co., Washington, DCCrossRefGoogle Scholar
  37. Mucha PJ, Richardson T, Macon K, Porter MA, Onnela JP (2010) Community structure in time-dependent, multiscale, and multiplex networks. Science 328(5980):876–878.  https://doi.org/10.1126/science.1184819MathSciNetCrossRefzbMATHGoogle Scholar
  38. Pepe A, Wolff S, Godtsenhoven KV (2011) One, none and one hundred thousand profiles: reimagining the pirandellian identity dilemma in the era of online social networks, arXiv:1109.3428Google Scholar
  39. Rainie L, Wellman B (2012) Networked: the new social operating system. MIT Press, CambridgeGoogle Scholar
  40. Rossi L, Magnani M (2015) Towards effective visual analytics on multiplex and multilayer networks. Chaos, Solitons, Fractals 72:68–76MathSciNetCrossRefGoogle Scholar
  41. Stefanone M, Kwon K, Lackaff D (2011) The value of online friends: networked resources via social network sites. First Monday 16(2)Google Scholar
  42. Sun Y, Han J, Zhao P, Yin Z, Cheng H, Wu T (2009) RankClus. In: Proceedings of the 12th international conference on extending database technology advances in database technology – EDBT’09, ACM Press, New York, p. 565.  https://doi.org/10.1145/1516360.1516426
  43. Sun Y, Han J, Aggarwal CC, Chawla NV (2012) When will it happen? In: Proceedings of the fifth ACM international conference on Web search and data mining – WSDM’12, ACM Press, New York, p. 663.  https://doi.org/10.1145/2124295.2124373
  44. Szell M, Lambiotte R, Thurner S (2010) Multirelational organization of large-scale social networks in an online world. PNAS 107(31):13636–13641CrossRefGoogle Scholar
  45. Tang L, Liu H (2009) Relational learning via latent social dimensions. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, ACM Press, New York, KDD’09, pp. 817–826.  https://doi.org/10.1145/1557019.1557109
  46. Turkle S (1995) Life on the screen: identity in the age of the internet. Simon & Schuster, New YorkGoogle Scholar
  47. Verbrugge LM (1979) Multiplexity in adult friendships. Soc Forces 57(4):1286–1309.  https://doi.org/10.1093/sf/57.4.1286CrossRefGoogle Scholar
  48. Yin X, Han J, Yu PS (2006) LinkClus: efficient clustering via heterogeneous semantic links. In: Proceedings of the 32nd international conference on very large data bases, VLDB Endowment, VLDB’06 Endowment, pp. 427–438Google Scholar

Copyright information

© Springer Science+Business Media LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Information TechnologyUppsala UniversityUppsalaSweden
  2. 2.University of CopenhagenKøbenhavnDenmark

Section editors and affiliations

  • Fabrizio Silvestri
    • 1
  • Andrea Tagarelli
    • 2
  1. 1.Yahoo IncLondonUK
  2. 2.University of CalabriaArcavacata di RendeItaly