A Comparison of Statistical and Machine Learning Algorithms on the Task of Link Completion
Workshop Paper, KDD '03 Workshop on Link Analysis for Detecting Complex Behavior (LinkKDD '03), August, 2003
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.
BibTeX
@workshop{Goldenberg-2003-119844,author = {A. Goldenberg and J. Kubica and P. Komarek and A. Moore and J. Schneider},
title = {A Comparison of Statistical and Machine Learning Algorithms on the Task of Link Completion},
booktitle = {Proceedings of KDD '03 Workshop on Link Analysis for Detecting Complex Behavior (LinkKDD '03)},
year = {2003},
month = {August},
}
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