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Maximum Likelihood Methods for Data Mining in Datasets Represented by Graphs

Overview Due to the recent boom in complex network research, large graph datasets appeared in various fields, from social sciences to computer science and biology. There is an increasing demand for data mining methods that allow scientists to make sense of the datasets they encounter. This paper presents two graph models and two maximum likelihood algorithms that fit these models to pre-defined data. They 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.

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Publisher
Institute of Electrical and Electronics Engineers
File Format
PDF
Date Published
Oct 7, 2008
Format
White Papers
Topics
Presentation Software, Data Mining - Analysis

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