Abstract
We present an algorithm for learning correlations among link types and node attributes in relational data that represent complex networks. The link correlations are represented in a Bayes net structure. This provides a succinct graphical way to display relational statistical patterns and support powerful probabilistic inferences. The current state of the art algorithm for learning relational Bayes nets captures only correlations among entity attributes given the existence of links among entities. The models described in this paper capture a wider class of correlations that involve uncertainty about the link structure. Our base line method learns a Bayes net from join tables directly. This is a statistically powerful procedure that finds many correlations, but does not scale well to larger datasets. We compare join table search with a hierarchical search strategy.
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Qian, Z., Schulte, O. (2014). Learning Bayes Nets for Relational Data with Link Uncertainty. In: Croitoru, M., Rudolph, S., Woltran, S., Gonzales, C. (eds) Graph Structures for Knowledge Representation and Reasoning. Lecture Notes in Computer Science(), vol 8323. Springer, Cham. https://doi.org/10.1007/978-3-319-04534-4_9
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DOI: https://doi.org/10.1007/978-3-319-04534-4_9
Publisher Name: Springer, Cham
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