Skip to main content
SpringerLink
Log in

Discovery of Complex User Communities

  • Chapter
  • First Online:
User Community Discovery

Part of the book series: Human–Computer Interaction Series ((HCIS))

  • 525 Accesses

Abstract

This chapter serves as an introduction to the book on User Community Discovery, setting the scene for the presentation in the rest of the book of various methods for the discovery of user communities in the social Web. In this context, the current chapter introduces the various types of user community, as they appeared in the early days of the Web, and how they converged to the common concept of active user community in the social Web. In this manner, the chapter aims to clarify the use of terminology in the various research areas that study user communities. Additionally, the main approaches to discovering user communities are briefly introduced and a number of new challenges for community discovery in the social Web are highlighted. In particular we emphasize the complexity of the networks that are constructed among users and other entities in the social Web. Social networks are typically multi-modal, i.e. containing different types of entity, multi-relational, i.e. comprising different relation types, and dynamic, i.e. changing over time. The complexity of the networks calls for new versatile and efficient methods for community discovery. Details about such methods are provided in the rest of the book.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Although the term “community discovery” is more suitable to describe this process, throughout the text we adopt the term “community detection”, which is the prevalent term used in the literature to refer to this problem.

  2. 2.

    Centrality quantifies how often nodes belong to the paths connecting other nodes.

  3. 3.

    http://grouplens.org/datasets/hetrec-2011/.

  4. 4.

    http://digg.com/.

References

  1. Ahn Y-Y, Bagrow JP, Lehmann S (2010) Link communities reveal multiscale complexity in networks. Nature 466:761–764

    Article  Google Scholar 

  2. Amini AA, Chen A, Bickel PJ, Levina E (2013) Pseudo-likelihood methods for community detection in large sparse networks. Ann Stat 41(4):2097–2122

    Article  MathSciNet  MATH  Google Scholar 

  3. Arenas A, Díaz-Guilera A, Pérez-Vicente CJ (2006) Synchronization reveals topological scales in complex networks. Phys Rev Lett 96:114102

    Article  Google Scholar 

  4. Batagelj V, Zaversnik M (2003) An o(m) algorithm for cores decomposition of networks. CoRR, arXiv:cs.DS/0310049

  5. Berge C, Minieka E (1973) Graphs and hypergraphs, vol 7. North-Holland Publishing Company, Amsterdam

    Google Scholar 

  6. Bhattacharya P, Zafar MB, Ganguly N, Ghosh S, Gummadi KP (2014) Inferring user interests in the twitter social network. In: Proceedings of the 8th ACM conference on recommender systems, RecSys’14. ACM, New York, pp 357–360

    Google Scholar 

  7. Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech 2008(10):P10008

    Article  Google Scholar 

  8. Bonanich P, Holdren AC, Johnston M (2004) Hyper-edges and multidimensional centrality. Soc Netw 26:189–203

    Article  Google Scholar 

  9. Borgatti SP, Everett MG (1999) Models of core/periphery structures. Soc Netw 21:375–395

    Article  Google Scholar 

  10. Bouras C, Igglesis V, Kapoulas V, Tsiatsos T (2005) A web-based virtual community. IJWBC 1(2):127–139

    Article  Google Scholar 

  11. Boyd DM, Ellison NB (2007) Social network sites: definition, history, and scholarship. J Comput-Mediat Commun 13(1):210–230

    Article  Google Scholar 

  12. Breiger RL, Boorman SA, Arabie P (1975) An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling. J Math Psychol 12(3):328–383

    Article  Google Scholar 

  13. Bron C, Kerbosch J (1973) Algorithm 457: finding all cliques of an undirected graph. Commun ACM 16(9):575–577 September

    Article  MATH  Google Scholar 

  14. Cai D, Shao Z, He X, Yan X, Han J (2005) Community mining from multi-relational networks. Knowledge discovery in databases: PKDD 2005. Springer, New York, pp 445–452

    Chapter  Google Scholar 

  15. Cattuto C, Baldassarri A, Servedio VDP, Loreto V (2008) Emergent community structure in social tagging systems. Adv Complex Syst 11(4):597–608

    Article  MATH  Google Scholar 

  16. Chakrabarti D (2004) Autopart: parameter-free graph partitioning and outlier detection. In: Boulicaut J-F, Esposito F, Giannotti F, Pedreschi D (eds) PKDD, Lecture Notes in Computer Science, vol 3202. Springer, New York, pp 112–124

    Google Scholar 

  17. Chen J, Zaïane O, Goebel R (2009) A visual data mining approach to find overlapping communities in networks. Proceedings of the 2009 international conference on advances in social network analysis and mining, ASONAM’09. IEEE Computer Society, Washington, pp 338–343

    Chapter  Google Scholar 

  18. Clauset A (2005) Finding local community structure in networks. Phys Rev E 72:026132

    Article  Google Scholar 

  19. Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 1–6

    Google Scholar 

  20. Cooley R, Mobasher B, Srivastava J (1997) Web mining: Information and pattern discovery on the world wide web. In: Proceedings of the nineth international conference on tools with artificial intelligence (ICTAI). Newport Beach, pp 558–567

    Google Scholar 

  21. Danon L, Guilera AD, Duch J, Arenas A (2005) Comparing community structure identification. J Stat Mech: Theory Exp 2005(9):P09008–P09008

    Article  Google Scholar 

  22. Dawande M, Keskinocak P, Swaminathan JM, Tayur S (2001) On bipartite and multipartite clique problems. J Algorithm 41(2):388–403

    Article  MathSciNet  MATH  Google Scholar 

  23. Donetti L, Munoz MA (2004) Detecting network communities: a new systematic and efficient algorithm. J Stat Mech 2004:P10012

    Article  MATH  Google Scholar 

  24. Duch J, Arenas A (2005) Community detection in complex networks using extremal optimization. Phys Rev E 72:027104

    Article  Google Scholar 

  25. Esposito F, Ferilli S, Basile T, Di Mauro N (2012) Social networks and statistical relational learning: a survey. Int J Soc Netw Min 1(2):185–208

    Article  Google Scholar 

  26. Flake G, Lawrence S, Lee Giles C (2000) Efficient identification of web communities. In: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining, KDD’00. ACM, New York, pp 150–160

    Google Scholar 

  27. Fortunato S (2010) Community detection in graphs. Phys Rep 486:75–174

    Article  MathSciNet  Google Scholar 

  28. Fortunato S, Latora V, Marchiori M (2004) Method to find community structures based on information centrality. Phys Rev E 70(5):056104

    Article  Google Scholar 

  29. Garas A, Schweitzer F, Havlin S (2012) A k-shell decomposition method for weighted networks. New J Phys 14(8):083030

    Article  Google Scholar 

  30. Gargi U, Lu W, Mirrokni VS, Yoon S (2011) Large-scale community detection on youtube for topic discovery and exploration. In: Adamic LA, Baeza-Yates RA, Counts S (eds) ICWSM. The AAAI Press, Palo Alto

    Google Scholar 

  31. Gauvin L, Panisson A, Cattuto C (2013) Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach. CoRR. arXiv:abs/1308.0723

  32. Gauvin L, Panisson A, Cattuto C (2014) Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach. PLoS ONE 9(1):e86028

    Article  Google Scholar 

  33. Getoor L, Taskar B (2007) Introduction to statistical relational learning. MIT Press, Cambridge

    MATH  Google Scholar 

  34. Ghosh S, Kane P, Ganguly N (2011) Identifying overlapping communities in folksonomies or tripartite hypergraphs. In: Proceedings of the 20th international conference companion on world wide web. ACM, pp 39–40

    Google Scholar 

  35. Giatsoglou M, Vakali A (2013) Capturing social data evolution using graph clustering. IEEE Internet Comput 17(1):74–79

    Article  Google Scholar 

  36. Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826

    Article  MathSciNet  MATH  Google Scholar 

  37. Gregory S (2010) Finding overlapping communities in networks by label propagation. New J Phys 12(10):103018

    Article  Google Scholar 

  38. Guimerà R, Sales-Pardo M, Amaral L (2007) Module identification in bipartite and directed networks. Phys Rev Lett 76(3):036102

    Google Scholar 

  39. Hastings MB (2006) Community detection as an inference problem. Phys Rev E 74:035102

    Article  Google Scholar 

  40. Hill WC, Stead L, Rosenstein M, Furnas GW (1995) Recommending and evaluating choices in a virtual community of use. In: Proceedings of the conference on human factors in computing systems (CHI). Denver, Colorado, pp 194–201

    Google Scholar 

  41. Holland PW, Laskey KB, Leinhardt S (1983) Stochastic blockmodels: first steps. Soc Netw 5(2):109–137

    Article  MathSciNet  Google Scholar 

  42. Holme P, Saramäki J (2012) Temporal networks. Phys Rep 519(3):97–125

    Article  Google Scholar 

  43. Holme P, Saramäki J (2013) Temporal networks. Springer, New York

    Book  Google Scholar 

  44. Ino H, Kudo M, Nakamura A (2005) Partitioning of web graphs by community topology. In: WWW. ACM, New York, pp 661–669

    Google Scholar 

  45. Jacquin A, Misakova L, Gay M (2008) A hybrid object-based classification approach for mapping urban sprawl in periurban environment. Landsc Urban Plan 84(2):152–165

    Article  Google Scholar 

  46. Jones S, O’Neill E (2010) Feasibility of structural network clustering for group-based privacy control in social networks. In: Proceedings of the sixth symposium on usable privacy and security, SOUPS’10. ACM, New York, pp 9:1–9:13

    Google Scholar 

  47. Kannan R, Vempala S, Vetta A (2004) On clusterings: good, bad and spectral. J ACM 51(3):497–515 May

    Article  MathSciNet  MATH  Google Scholar 

  48. Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA (2014) Multilayer networks. J Complex Netw 2(3):203–271

    Article  Google Scholar 

  49. Konstan JA, Riedl J (2012) Recommender systems: from algorithms to user experience. User modeling and user-adapted interaction 22 (this issue)

    Google Scholar 

  50. Konstan JA, Miller BN, Maltz D, Herlocker JL, Gordon LR, Riedl J (1997) GroupLens: applying collaborative filtering to usenet news. Commun ACM 40(3):77–87

    Article  Google Scholar 

  51. Kovács IA, Palotai R, Szalay MS, Csermely P (2010) Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics. PLoS ONE 5(9):e12528

    Article  Google Scholar 

  52. Kumar R, Raghavan P, Rajagopalan S, Tomkins A (1999) Trawling the web for emerging cyber-communities. Comput Netw 31(11–16):1481–1493

    Article  Google Scholar 

  53. Lancichinetti A, Radicchi F, Ramasco JJ, Fortunato S (2011) Finding statistically significant communities in networks. PLoS ONE 6(5):e18961

    Article  Google Scholar 

  54. Leung IXY, Hui P, Liò P, Crowcroft J (2009) Towards real-time community detection in large networks. Phys Rev E 79:066107

    Article  Google Scholar 

  55. Li X, Wu C, Zach C, Lazebnik S, Frahm J-M (2008) Modeling and recognition of landmark image collections using iconic scene graphs. In: Proceedings of the 10th European conference on computer vision: part I, ECCV’08. Springer, Berlin, pp 427–440

    Google Scholar 

  56. Lin Y-R, Sun J, Castro P, Konuru R, Sundaram H, Kelliher A (2009) MetaFac: community discovery via relational hypergraph factorization. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’09. ACM, pp 527–536

    Google Scholar 

  57. Lin Y-R, Sundaram H, Kelliher A (2009) JAM: joint action matrix factorization for summarizing a temporal heterogeneous social network. In: Proceedings of the third international conference on weblogs and social media, ICWSM 2009. San Jose, California, 17–20 May 2009

    Google Scholar 

  58. Luo F, Wang JZ, Promislow E (2006) Exploring local community structures in large networks. In: Proceedings of the 2006 IEEE/WIC/ACM international conference on web intelligence, WI’06. IEEE Computer Society, Washington

    Google Scholar 

  59. Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416

    Article  MathSciNet  Google Scholar 

  60. Malliaros FD, Vazirgiannis M (2013) Clustering and community detection in directed networks: a survey. Phys Rep 533:95–142

    Article  MathSciNet  MATH  Google Scholar 

  61. Massen CP, Doye JPK (2005) Identifying communities within energy landscapes. Phys Rev E 71:046101

    Article  Google Scholar 

  62. Mcauley J, Leskovec J (2014) Discovering social circles in Ego networks. ACM Trans Knowl Discov Data 8(1):4:1–4:28

    Article  Google Scholar 

  63. Miorandi D, De Pellegrini F (2010) K-shell decomposition for dynamic complex networks. In: 2010 Proceedings of the 8th international symposium on modeling and optimization in mobile, ad hoc and wireless networks (WiOpt). IEEE, pp 488–496

    Google Scholar 

  64. Moëllic P-A, Haugeard J-E, Pitel G (2008) Image clustering based on a shared nearest neighbors approach for tagged collections. In: Proceedings of the 2008 international conference on content-based image and video retrieval, CIVR’08. ACM, New York, pp 269–278

    Google Scholar 

  65. Murata T (2009) Modularities for bipartite networks. In: Proceedings of the 20th ACM conference on hypertext and hypermedia. ACM, pp 245–250

    Google Scholar 

  66. Murata T (2010) Detecting communities from tripartite networks. In: Proceedings of the 19th international conference on world wide web. ACM, pp 1159–1160

    Google Scholar 

  67. Murata T (2011) Detecting communities from social tagging networks based on tripartite modularity. In: Proceedings of the workshop on link analysis in heterogeneous information networks

    Google Scholar 

  68. Neubauer N, Obermayer K (2010) Community detection in tagging-induced hypergraphs. Workshop on information in networks. New York University, New York, pp 24–25

    Google Scholar 

  69. Newman M (2010) Networks: an introduction. Oxford University Press, Oxford

    Book  MATH  Google Scholar 

  70. Newman MEJ (2003) Fast algorithm for detecting community structure in networks. Phys Rev E 69:066133

    Article  Google Scholar 

  71. Newman MEJ (2006) Finding community structure in networks using the eigenvectors of matrices. Phys Rev E 74(3):036104

    Article  MathSciNet  Google Scholar 

  72. Newman MEJ (2013) Spectral methods for community detection and graph partitioning. Phys Rev E 88:042822

    Article  Google Scholar 

  73. Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69:026113

    Article  Google Scholar 

  74. Orwant J (1995) Heterogeneous learning in the doppelgänger user modeling system. User Model User-Adapt Interact 4(2):107–130

    Article  Google Scholar 

  75. Paliouras G (2012) Discovery of web user communities and their role in personalization. User Model User-Adapt Interact 22(1–2):151–175

    Article  Google Scholar 

  76. Paliouras G, Papatheodorou C, Karkaletsis V, Spyropoulos CD (2000) Clustering the users of large web sites into communities. In: Proceedings of the seventeenth international conference on machine learning (ICML). Stanford, pp 719–726

    Google Scholar 

  77. Palla G, Barabasi A-L, Vicsek T (2007) Quantifying social group evolution. Nature 446(7136):664–667

    Article  Google Scholar 

  78. Palla G, Dernyi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043):814–818

    Article  Google Scholar 

  79. Papadopoulos S, Zigkolis C, Kompatsiaris Y, Vakali A (2011) Cluster-based landmark and event detection for tagged photo collections. IEEE MultiMed 18(1):52–63

    Article  Google Scholar 

  80. Papadopoulos S, Kompatsiaris Y, Vakali A, Spyridonos P (2012) Community detection in social media. Data Min Knowl Discov 24(3):515–554

    Article  Google Scholar 

  81. Pons P, Latapy M (2005) Computing communities in large networks using random walks (long version). Computer and information sciences-ISCIS 2005, pp 284–293. arXiv:physics/0512106v1

  82. Porter MA, Onnela J-P, Mucha PJ (2009) Communities in networks. Not Am Math Soc 56(9):1082–1097

    MathSciNet  MATH  Google Scholar 

  83. Qi G-J, Aggarwal CC, Huang TS (2012) Community detection with edge content in social media networks. In: IEEE 28th international conference on data engineering (ICDE 2012), pp 534–545

    Google Scholar 

  84. Quillian MR (1968) Semantic memory. In: Minsky M (ed) Semantic information processing. MIT Press, Cambridge, pp 27–70

    Google Scholar 

  85. Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D (2004) Defining and identifying communities in networks. Proc Natl Acad Sci 101(9):2658

    Article  Google Scholar 

  86. Raghavan UN, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E 76:036106

    Article  Google Scholar 

  87. Ramadan E, Tarafdar A, Pothen A (2004) A hypergraph model for the yeast protein complex network. Proceedings of the 18th international parallel and distributed processing symposium, 2004. IEEE, p 189

    Google Scholar 

  88. Rêgo Drumond L, Diaz-Aviles E, Schmidt-Thieme L, Nejdl W (2014) Optimizing multi-relational factorization models for multiple target relations. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management, CIKM’14, pp 191–200

    Google Scholar 

  89. Reichardt J, Bornholdt S (2006) Statistical mechanics of community detection. Phys Rev E 74:016110

    Article  MathSciNet  Google Scholar 

  90. Rheingold H (1993) The virtual community: homesteading on the electronic frontier. Addison-Wesley, New York

    Google Scholar 

  91. Richens RH (1956) General program for mechanical translation between any two languages via an algebraic interlingua. Mech Transl 3(2):37

    Google Scholar 

  92. Rombach MP, Porter MA, Fowler JH, Mucha PJ (2014) Core-periphery structure in networks. SIAM J Appl Math 74(1):167–190

    Article  MathSciNet  MATH  Google Scholar 

  93. Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci 105(4):1118–1123

    Article  Google Scholar 

  94. Schaeffer SE (2007) Graph clustering. Comput Sci Rev 1(1):27–64

    Article  MATH  Google Scholar 

  95. Schuler D (1994) Community networks: building a new participatory medium. Commun ACM 37(1):38–51

    Article  Google Scholar 

  96. Scripps J, Tan P-N, Esfahanian A-H (2007) Node roles and community structure in networks. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on web mining and social network analysis, WebKDD/SNA-KDD’07. ACM, New York, pp 26–35

    Chapter  Google Scholar 

  97. Seidman SB (1983) Network structure and minimum degree. Soc Netw 5(3):269–287

    Article  MathSciNet  Google Scholar 

  98. Shardanand U, Maes P (1995) Social information filtering: algorithms for automating “word of mouth”. In: Proceedings of the conference on human factors in computing systems (CHI). Denver, Colorado, pp 210–217

    Google Scholar 

  99. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905

    Article  Google Scholar 

  100. Staab S, Angele J, Decker S, Erdmann M, Hotho A, Maedche A, Schnurr H-P, Studer R, Sure Y (2000) Semantic community web portals. Comput Netw 33(1–6):473–491

    Article  Google Scholar 

  101. Takaffoli M, Sangi F, Fagnan J, Zäıane O (2011) Community evolution mining in dynamic social networks. Procedia-Soc Behav Sci 22:49–58

    Article  Google Scholar 

  102. Tang L, Liu H (2010) Community detection and mining in social media (synthesis lectures on data mining and knowledge discovery). Morgan & Claypool Publishers, San Rafael

    Google Scholar 

  103. Tang J, Hu X, Liu H (2013) Social recommendation: a review. Soc Netw Anal Min 3(4):1113–1133

    Article  Google Scholar 

  104. van Dongen S (2000) Graph clustering by flow simulation. Ph.D. thesis, University of Utrecht

    Google Scholar 

  105. Vragović I, Louis E (2006) Network community structure and loop coefficient method. Phys Rev E 74:016105

    Article  Google Scholar 

  106. Wang J, Lee TT (1998) Paths and cycles of hypergraphs. Sci China 42(1):1–12

    Article  MathSciNet  MATH  Google Scholar 

  107. Wasserman S, Faust K (1994) Social network analysis: methods and applications, vol 8. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  108. Xie J, Kelley S, Szymanski BK (2013) Overlapping community detection in networks: the state-of-the-art and comparative study. ACM Comput Surv 45(4):43:1–43:35

    Article  MATH  Google Scholar 

  109. Xu X, Yuruk N, Feng Z, Schweiger TAJ (2007) Scan: a structural clustering algorithm for networks. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’07. ACM, New York, pp 824–833

    Chapter  Google Scholar 

  110. Xu Z, Tresp V, Rettinger A, Kersting K (2010) Social network mining with nonparametric relational models. Revised selected papers of the second international workshop on advances in social network mining and analysis (SNAKDD). Lecture notes in computer science, vol 5498. Springer, New York, pp 77–96

    Google Scholar 

  111. Yang J, McAuley J J, Leskovec J (2013) Community detection in networks with node attributes. International conference on data mining

    Google Scholar 

  112. Zhang H, Kan M-Y, Liu Y, Ma S (2014) Online social network profile linkage. Information retrieval technology. Springer, New York, pp 197–208

    Google Scholar 

  113. Zheleva E, Getoor L (2009) To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles. In: Proceedings of the 18th international conference on world wide web, WWW’09. ACM, New York, pp 531–540

    Google Scholar 

  114. Zhou D, Huang J, Schölkopf B (2006) Learning with hypergraphs: clustering, classification, and embedding. In: Advances in neural information processing systems, pp 1601–1608

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Informatics and Telecommunications, NCSR “Demokritos”, Patr. Grigoriou and Neapoleos Street, 15310, Aghia paraskevi, Attiki, Greece

    Georgios Paliouras & Dimitrios Vogiatzis

  2. Information Technologies Institute, Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece

    Symeon Papadopoulos

  3. The American College of Greece, Athens, Greece

    Dimitrios Vogiatzis

Authors
  1. Georgios Paliouras
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Symeon Papadopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dimitrios Vogiatzis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Georgios Paliouras .

Editor information

Editors and Affiliations

  1. Inst.of Informatics and Tele- communications Nat. Center for, Ag.Paraskevi, Greece

    Georgios Paliouras

  2. Information Technologies Institute, CERTH, Thermi, Thessaloniki, Greece

    Symeon Papadopoulos

  3. Institute of Informatics, NCSR Demokritos, Aghia Paraskevi, Greece

    Dimitrios Vogiatzis

  4. Informatics and Telematics Institut Centre for Research & Technology He, Thermi-Thessaloniki, Greece

    Yiannis Kompatsiaris

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Paliouras, G., Papadopoulos, S., Vogiatzis, D. (2015). Discovery of Complex User Communities. In: Paliouras, G., Papadopoulos, S., Vogiatzis, D., Kompatsiaris, Y. (eds) User Community Discovery. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-23835-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23835-7_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23834-0

  • Online ISBN: 978-3-319-23835-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation