Encyclopedia of Social Network Analysis and Mining

2018 Edition
| Editors: Reda Alhajj, Jon Rokne

Collective Intelligence: Overview

  • Ioannis Kompatsiaris
  • Sotiris Diplaris
  • Symeon Papadopoulos
Reference work entry
DOI: https://doi.org/10.1007/978-1-4939-7131-2_106



Community detection

A class of network analysis algorithms that identify groups of nodes that are densely connected

Data mining

Extracting implicit information from a domain


A set of nodes and edges connecting the nodes


A kind of analysis involving more than one media or metadata types (e.g., text, image, geolocation)


A graph that assigns some semantics to the nodes and kind of interaction for the links


Social network analysis is the study of social network characteristics and dynamics


User-generated multimedia content (image, text) that is created/captured by casual users and shared online


Recent advances of Web technologies have effectively turned ordinary people into active members of the Web: casual users act as co-developers, and their interactions and collaborations with each other have added a new social dimension on Web data. For example, Wikipedia (http://www.wikipedia.org...

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



The work presented in this article was supported by the European Commission under contracts FP7-215453 WeKnowIt and FP7-287975 SocialSensor.


  1. Ahmed M, Spagna S, Huici F, Niccolini F (2013) A peek into the future: predicting the evolution of popularity in user generated content. In: Proceedings of the 6th ACM international conference web search and data mining, pp 607–616Google Scholar
  2. Aiello LM, Petkos G, Martin C, Corney D, Papadopoulos S, Skraba R, Göker A, Kompatsiaris I, Jaimes A (2013) Sensing trending topics in Twitter. Trans Multimed 15(6):1268–1282.  https://doi.org/10.1109/TMM.2013.2265080CrossRefGoogle Scholar
  3. Arapakis I, Barla Cambazoglu B, Lalmas M (2014) On the feasibility of predicting news popularity at cold start. In Prpc. 6th international conference, SocInfo 2014, Barcelona, Spain, 11–13 November 2014, pp 290–299Google Scholar
  4. Au Yeung CM, Gibbins N, Shadbolt N (2009) Contextualising tags in collaborative tagging systems. In: HT ‘09: proceedings of 20th ACM conference on hypertext and hypermedia, pp 251–260Google Scholar
  5. Becker H, Naaman M, Gravano L (2010) Learning similarity metrics for event identification in social media. In: Proceedings of the third ACM international conference on web search and data mining, WSDM ‘10. ACM, New York, pp 291–300Google Scholar
  6. Cai X, Nie F, Huang H, Kamangar F (2011) Heterogeneous image feature integration via multi-modal spectral clustering. In: 2011 I.E. conference on computer vision and pattern recognition (CVPR), pp 1977–1984.  https://doi.org/10.1109/CVPR.2011.5995740
  7. Chiarandini L, Grabowicz PA, Trevisiol M, Jaimes A (2013) Leveraging browsing patterns for topic discovery and photostream recommendation. In ICWSM‘13: 7th international AAAI conference on weblogs and social media, Boston, USAGoogle Scholar
  8. De Choudhury M, Feldman M, Amer S, Golbandi N, Lempel R, Yu C (2011) Automatic construction of travel itineraries using social breadcrumbs. Proceedings of 21st ACM conference on hypertext and hypermedia, pp 35–44Google Scholar
  9. Gemmell J, Shepitsen A, Mobasher B, Burke R (2008) Personalizing navigation in folksonomies using hierarchical tag clustering. In: DaWaK ‘08: proceedings of 10th international conference on data warehousing and knowledge discovery, pp 196–205Google Scholar
  10. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L (2009) Detecting influenza epidemics using search engine query data. Nature 457:1012–1014CrossRefGoogle Scholar
  11. Girardin F, Calabrese F, Dal Fiore F, Ratti C, Blat J (2008) Digital footprinting: uncovering tourists with user-generated content. IEEE Pervasive Comput 7(4):36–43CrossRefGoogle Scholar
  12. Henrich A, Lüdecke V (2008) Determining geographic representations for arbitrary concepts at query time. In: Proceedings of first international workshop on location and the web, pp 17–24Google Scholar
  13. Hinze A, Voisard A (2003) Location and time-based information delivery in tourism, advances in spatial and temporal databases. Lect Notes Comput Sci 2750:489–507CrossRefGoogle Scholar
  14. Jeon J, Lavrenko V, Manmatha R (2003) Automatic image annotation and retrieval using cross-media relevance models. Proceedings of 26th annual international ACM SIGIR conference on research and development in information retrieval, pp 119–126Google Scholar
  15. Jin X, Gallagher A, Cao L, Luo J, Han J (2010) The wisdom of social multimedia: using Flickr for prediction and forecast. MM ‘10 proceedings of international conference on multimedia, pp 1235–1244Google Scholar
  16. Kalantidis Y, Tolias G, Avrithis Y, Phinikettos M, Spyrou E, Mylonas P, Kollias S (2011) VIRaL: visual image retrieval and localization. Multimed Tools Appl 51(2):555–592CrossRefGoogle Scholar
  17. Kendall T, Zhou D (2009) Leveraging information in a social network for inferential targeting of advertisements, US Patent App. 12/419,958Google Scholar
  18. Kennedy LS, Naaman M, Ahern S, Nair R, Rattenbury T (2007) How Flickr helps us make sense of the world: context and content in community-contributed media collections. In: Proceedings of the ACM multimedia ‘07, pp 631–640Google Scholar
  19. Li J, Wang JZ (2003) Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans Pattern Anal Mach Intell 25:1075–1088CrossRefGoogle Scholar
  20. Lin YR, Candan KS, Sundaram H, Xie L (2011) Scent: scalable compressed monitoring of evolving multi-relational social networks. ACM Trans Multimedia Comput Commun App 2(3):1–25Google Scholar
  21. Liu D, Hua XS, Wang M, Zhang HJ (2010) Retagging social images based on visual and semantic consistency. In: Proceedings of 19th international conference on world wide web, WWW’10, pp 1149–1150Google Scholar
  22. Macskassy SA, Provost F (2007) Classification in networked data: a toolkit and a univariate case study. J Mach Learn Res 8:935–983Google Scholar
  23. Martin-Borregon D, Aiello LM, Grabowicz P, Jaimes A, Baeza-Yates R (2014) Characterization of online groups along space, time, and social dimensions. EPJ Data Sci 2014:8CrossRefGoogle Scholar
  24. Nikolopoulos ., Giannakidou E, Kompatsiaris I, Patras I, Vakali A (2011, in press) Combining multi-modal features for social media analysis. In: Hoi S, Luo J, Boll S, Xu D, Jin R, King I (eds) Social media modeling and computing. Springer, Berlin, pp 71–96CrossRefGoogle Scholar
  25. Papadopoulos S, Zigkolis C, Kompatsiaris Y, Vakali A (2011a) Cluster-based landmark and event detection on tagged photo collections. IEEE Multimed 18(1):52–63CrossRefGoogle Scholar
  26. Papadopoulos S, Zigkolis C, Kapiris S, Kompatsiaris Y, Vakali A (2011b) City exploration by use of spatio-temporal analysis and clustering of user contributed photos. Demo paper in ACM international conference on multimedia retrieval (ICMR), pp 65:1–65:2Google Scholar
  27. Petkos G, Schinas M, Papadopoulos S, Kompatsiaris I (2016) Graph-based multimodal clustering for social multimedia. Multimed Tools Appl.  https://doi.org/10.1007/s11042-016-3378-2
  28. Quack T, Leibe B, Van Gool L (2008) World-scale mining of objects and events from community photo collections. In: Proceedings of the international conference on content-based image and video retrieval, pp 47–56Google Scholar
  29. Quercia D, Schifanella R, Aiello LM (2014) The shortest path to happiness: recommending beautiful, quiet and happy routes in the city. In: Proceedings of the 25th ACM conference on hypertext and social media (HT ‘14). ACM, New York, NY, USA, pp 116–125.  https://doi.org/10.1145/2631775.2631799
  30. Sakaki T, Okazaki M, Matsuo Y (2010) Earthquake shakes Twitter users: real-time event detection by social sensors. In: World wide web conference, pp 851–860Google Scholar
  31. Schifanella R, Barrat A, Cattuto C, Markines B, Menczer F (2010) Folks in folksonomies: social link prediction from shared metadata. In: WSDM ‘10: Proceedings of the 3rd ACM international conference on web search and data mining, pp 271–280Google Scholar
  32. Signorini A (2011) Swine Flu monitoring using Twitter. http://compepi.cs.uiowa.edu/ alessio/twitter − monitor − swine − flu/. Accessed 25 Oct 2011
  33. Specia L, Motta E (2007) Integrating folksonomies with the semantic web. In: ESWC ‘07: proceedings of 4th European conference on The semantic web, pp 624–639Google Scholar
  34. Tang L, Liu H (2011) Leveraging social media networks for classification. Data Min Knowl Disc 23:447–478MathSciNetzbMATHCrossRefGoogle Scholar
  35. Tsakalidis A, Papadopoulos S, Kompatsiaris I, (2014) An ensemble model for cross-domain polarity classification on Twitter. In: Proceedings of web information systems engineering – WISE 2014, Springer, pp. 168–177CrossRefGoogle Scholar
  36. Wen Z, Lin CY (2010) On the quality of inferring interests from social neighbors. In: Proceedings of 16th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’10), pp 373–382Google Scholar
  37. Wu X, Ngo CW, Hauptmann AG, Tan HK (2009) Real-time near-duplicate elimination for web video search with content and context. Multimed IEEE Trans 11(2):196–207CrossRefGoogle Scholar
  38. Yang YH, Wu PT, Lee CW, Lin KH, Hsu WH, Chen HH (2008) ContextSeer: context search and recommendation at query time for shared consumer photos. In: Proceedings of the 16th ACM international conference on multimedia (MM ‘08), pp 199–208Google Scholar
  39. Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. SIGMOD Rec 25(2):103–114CrossRefGoogle Scholar
  40. Zhou D, Bousquet O, Lal TN, Weston J, Schölkopf B (2004) Learning with local and global consistency. Adv NIPS 16:321–328Google Scholar
  41. Zigkolis C, Papadopoulos S, Filippou G et al (2014) Multimed Tools Appl 70:89.  https://doi.org/10.1007/s11042-012-1154-5CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Ioannis Kompatsiaris
    • 1
  • Sotiris Diplaris
    • 1
  • Symeon Papadopoulos
    • 1
  1. 1.Centre for Research and Technology HellasInformation Technologies InstituteThessalonikiGreece

Section editors and affiliations

  • Thomas Gottron
    • 1
  • Stefan Schlobach
    • 2
  • Steffen Staab
    • 3
  1. 1.Institute for Web Science and TechnologiesUniversität Koblenz-LandauKoblenzGermany
  2. 2.YUAmsterdamThe Netherlands
  3. 3.Institute for Web Science and TechnologiesUniversität Koblenz-LandauKoblenzGermany