Abstract
In this paper we differentiate between hard and soft label propagation for classification of relational (networked) data. The latter method assigns probabilities or class-membership scores to data instances, then propagates these scores throughout the networked data, whereas the former works by explicitly propagating class labels at each iteration. We present a comparative empirical study of these methods applied to a relational binary classification task, and evaluate two approaches on both synthetic and real–world relational data. Our results indicate that while neither approach dominates the other over the entire range of input data parameters, there are some interesting and non–trivial tradeoffs between them.
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Galstyan, A., Cohen, P.R. (2008). Empirical Comparison of “Hard” and “Soft” Label Propagation for Relational Classification. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds) Inductive Logic Programming. ILP 2007. Lecture Notes in Computer Science(), vol 4894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78469-2_13
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DOI: https://doi.org/10.1007/978-3-540-78469-2_13
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