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Collective Classification

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  • First Online:
Encyclopedia of Social Network Analysis and Mining

Synonyms

Iterative classification; Link-based classification; Node classification; Relational learning; User classification

Glossary

Class:

Is used to name a collection of network nodes that reasonably might be grouped together. Commonly encountered classes have simple unique textual descriptions – labels

Classification:

Means assigning network nodes into predefined classes – giving them class labels (labelling). Having data about nodes that are already labelled (known nodes), we can derive some knowledge – train a classifier. A trained classifier is able to map (classify) the features of unknown nodes to classes – assign the class labels. For instance, we would like to allocate humans to those with and without disease based on their symptoms and illnesses of their parents. The common classification tasks accomplish either binary or multi-class classification. It means that a given data instance may be assigned with one of two or one of many classes, respectively

Features (Attributes):

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References

  • Besag J (1993) Statistical analysis of dirty pictures. J Appl Stat 20(5):63–87

    Google Scholar 

  • Bilgic M, Getoor L (2008) Effective label acquisition for collective classification. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, Las Vegas, pp 43–51. doi:10.1145/1401890.1401901

    Google Scholar 

  • Bilgic M, Getoor L (2010) Active inference for collective classification. In: Fox M, Poole D (eds) AAAI, Atlanta. AAAI Press

    Google Scholar 

  • Bilgic M, Namata G, Getoor L (2007) Combining collective classification and link prediction, pp 381–386. doi:10.1109/ICDMW.2007.35

    Google Scholar 

  • Cataltepe Z, Sonmez A, Baglioglu K, Erzan A (2011) Collective classification using heterogeneous classifiers. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). LNAI 6871:155–169

    Google Scholar 

  • Chakrabarti S, Dom B, Indyk P (1998) Enhanced hypertext categorization using hyperlinks. SIGMOD Rec 27(2):307–318. doi:10.1145/276305.276332

    Google Scholar 

  • Crane R, McDowell LK (2011) Evaluating markov logic networks for collective classification. In: Proceedings of the 9th MLG workshop at the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, San Diego

    Google Scholar 

  • Eldardiry H, Neville J (2010) Multi-network fusion for collective inference. In: Proceedings of the eighth workshop on mining and learning with graphs, MLG '10, Washington, DC. ACM, New York, pp 46–54

    Google Scholar 

  • Eldardiry H, Neville J (2011) Across-model collective ensemble classification. In: Association for the advancement of artificial intelligence, San Francisco

    Google Scholar 

  • Fast A, Jensen D (2008) Why stacked models perform effective collective classification. In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, pp 785–790

    Google Scholar 

  • Geman S, Geman D (1984) Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6:721–741

    MATH  Google Scholar 

  • Gupta K, Aha D, Moore P (2009) Case-based collective inference for maritime object classification. Lecture Notes of Computer Science, LNAI 5650:434–449

    Google Scholar 

  • Hummel R, Zucker S (1983) On the foundations of relaxation labeling processes. IEEE Trans Pattern Anal Mach Intell 5(3):267–287

    MATH  Google Scholar 

  • Indyk W, Kajdanowicz T, Kazienko P (2012b) Web spam detection using mapreduce approach to collective classification. Adv Intell Syst Comput 189:197–206

    Google Scholar 

  • Indyk W, Kajdanowicz T, Kazienko P (2013), Relational large scale multi-label classification method for video categorization, Multimedia Tools and Applications, vol 65(1), pp 63–74

    Google Scholar 

  • Jensen D, Neville J (2002) Linkage and autocorrelation cause feature selection bias in relational learning. In: Proceedings of the 19th international conference on machine learning, Sydney. Morgan Kaufmann, pp 259–266

    Google Scholar 

  • Kajdanowicz T, Indyk W, Kazienko P (2012a) Mapreduce approach to relational influence propagation in complex network. Pattern Anal Appl. doi:10.1007/s10044-012-0294-6

    Google Scholar 

  • Kajdanowicz T, Kazienko P, Janczak M (2012b) Collective classification techniques: an experimental study. In: ADBIS 2012 – 16th East-European conference on advances in databases and information systems, MCSD 2012 – mining complex and stream data, Poznan. Advances in intelligent systems and computing, vol 185. Springer, pp 99–108

    Google Scholar 

  • Kazienko P, Kajdanowicz T (2012) Label-dependent node classification in the network. Neurocomputing 75:199–209

    Google Scholar 

  • Kuwadekar A, Neville J (2011) Relational active learning for joint collective classification models. In: Proceedings of the 28th International Conference on Machine Learning, Bellevue, pp 385–392

    Google Scholar 

  • Laorden C, Sanz B, Santos I, Galán-García P, Bringas P (2011) Collective classification for spam filtering. Lecture Notes of Computer Science, LNCS 6694:1–8

    Google Scholar 

  • Li L, Ouyang J, Liu D, Yang B, Huang Y (2011) A combined collective classification method for sparse data. J Inf Comput Sci 8(15):3453–3461

    Google Scholar 

  • McDowell L, Aha D (2012) Semi-supervised collective classification via hybrid label regularization. In Proceedings of the 29th International Conference on Machine Learning, 975–982

    Google Scholar 

  • McDowell L, Gupta M, Aha D (2009) Cautious collective classification. J Mach Learn Res 10:2777–2836

    MATH  MathSciNet  Google Scholar 

  • McDowell L, Gupta K, Aha D (2010) Meta-prediction for collective classification. In: Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23, Daytona Beach, pp 38–43

    Google Scholar 

  • Namata GM, London B, Getoor L, Huang B (2012) Query-driven active surveying for collective classification. In: 10th International Workshop on Mining and Learning with Graphs

    Google Scholar 

  • Neville J, Jensen D (2000) Iterative classification in relational data. In: Proceeding of AAAI, Austin. AAAI Press, pp 13–20

    Google Scholar 

  • Neville J, Jensen D (2003) Collective classification with relational dependency networks. In: Proceedings of ACM SIGKDD, Conference on Knowledge Discovery and Data Mining, Washington, DC

    Google Scholar 

  • Pearl J (1988) Probabilistic reasoning in intelligent systems. Morgan Kaufmann, San Mateo

    Google Scholar 

  • Rosenfeld A, Hummel RA, Zucker SW (1976) Scene labeling by relaxation operations. IEEE Trans Syst Man Cybern 6(6):420–433

    MATH  MathSciNet  Google Scholar 

  • Santos I, Ugarte-Pedrero X, Sanz B, Laorden C, Bringas P (2011) Collective classification for packed executable identification, In: Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference, pp 23–30

    Google Scholar 

  • Sen P, Namata G, Bilgic M, Getoor L, Gallagher B, Eliassi-Rad T (2008) Collective classification in network data. AI Mag 29(3):93–106

    Google Scholar 

  • Taskar B, Abbeel P, Koller D (2002) Discriminative probabilistic models for relational data. In: Darwiche A, Friedman N (eds) UAI, Edmonton. Morgan Kaufmann, pp 485–492

    Google Scholar 

  • Wang X, Maghami M, Sukthankar G (2011) A robust collective classification approach to trust evaluation. In: AAMAS Workshop on Trust in Agent Societies, Taipei, pp 125–139

    Google Scholar 

  • Xiang R, Neville J (2011) Understanding propagation error and its effect on collective classification. In: Proceedings of the 2011 Eleventh IEEE International Conference on Data Mining, pp 834–843

    Google Scholar 

  • Yedidia J, Freeman W, Weiss Y (2000) Generalized belief propagation. In: Advances in Neural Information Processing Systems (NIPS), Denver, vol 13. MIT, pp 689–695

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Management, Institute of Informatics, Wrocław University of Technology, Wrocław, Poland

    Tomasz Kajdanowicz & Przemysław Kazienko

Authors
  1. Tomasz Kajdanowicz
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  2. Przemysław Kazienko
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

  2. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Jon Rokne

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Kajdanowicz, T., Kazienko, P. (2014). Collective Classification. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6170-8_45

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