Maximum likelihood methods for data mining in datasets represented by graphs

Tamás Nepusz, Fülöp Bazsó

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Due to the recent boom in complex network research, large graph datasets appeared in various fields, from social sciences [1] to computer science [2]-[4] and biology [5]. There is an increasing demand for data mining methods that allow scientists to make sense of the datasets they encounter. In this paper, we present two graph models and two maximum likelihood algorithms that fit these models to pre-defined data. We also show two example applications to illustrate that these algorithms are able to extract interesting and meaningful properties from the data represented by appropriate graphs.

Original languageEnglish
Title of host publication5th International Symposium on Intelligent Systems and Informatics, SISY 2007
Pages161-165
Number of pages5
DOIs
Publication statusPublished - Dec 1 2007
Event5th International Symposium on Intelligent Systems and Informatics, SISY 2007 - Subotica, Serbia
Duration: Aug 24 2007Aug 25 2007

Publication series

Name5th International Symposium on Intelligent Systems and Informatics, SISY 2007

Other

Other5th International Symposium on Intelligent Systems and Informatics, SISY 2007
CountrySerbia
CitySubotica
Period8/24/078/25/07

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Information Systems and Management
  • Control and Systems Engineering

Cite this

Nepusz, T., & Bazsó, F. (2007). Maximum likelihood methods for data mining in datasets represented by graphs. In 5th International Symposium on Intelligent Systems and Informatics, SISY 2007 (pp. 161-165). [4342644] (5th International Symposium on Intelligent Systems and Informatics, SISY 2007). https://doi.org/10.1109/SISY.2007.4342644