A Comparison of Statistical and Machine Learning Algorithms on the Task of Link Completion (2003)

Anna Goldenberg, Jeremy Kubica, Paul Komarek, Andrew Moore, Jeff Schneider

Tags

Applications, Efficient Statistical Algorithms, GDA, Link Analysis, Testing

Abstract

Link data, consisting of a collection of subsets of entities, can be an important source of information for a variety of fields including the social sciences, biology, criminology, and business intelligence. However, these links may be incomplete, containing one or more unknown members. We consider the problem of link completion, identifying which entities are the most likely missing members of a link given the previously observed links. We concentrate on the case of one missing entity. We compare a variety of recently developed along with standard machine learning and strawman algorithms adjusted to suit the task. The algorithms were tested extensively on a simulated and a range of real-world data sets.

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Approximate BibTeX Entry

@inproceedings{linkcomplete2003,
    Month = {August},
    Year = {2003},
    Booktitle = {KDD Workshop on Link Analysis for Detecting Complex Behavior},
    Author = { Anna Goldenberg, Jeremy Kubica, Paul Komarek, Andrew Moore, Jeff Schneider },
    Title = {A Comparison of Statistical and Machine Learning Algorithms on the Task of Link Completion}
}