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2011
ACM

Neural networks for relational learning: an experimental comparison

8 years 1 months ago
Neural networks for relational learning: an experimental comparison
In the last decade, connectionist models have been proposed that can process structured information directly. These methods, which are based on the use of graphs for the representation of the data and the relationships within the data, are particularly suitable for handling relational learning tasks. In this paper, two recently proposed architectures of this kind, i.e. Graph Neural Networks (GNNs) and Relational Neural Networks (RelNNs), are compared and discussed, along with their corresponding learning schemes. The goal is to evaluate the performance of these methods on benchmarks that are commonly used by the relational learning community. Moreover, we also aim at reporting differences in the behavior of the
Werner Uwents, Gabriele Monfardini, Hendrik Blocke
Added 14 May 2011
Updated 14 May 2011
Type Journal
Year 2011
Where ML
Authors Werner Uwents, Gabriele Monfardini, Hendrik Blockeel, Marco Gori, Franco Scarselli
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