# Out-of-Sample Representation Learning for Multi-Relational Graphs

@article{Albooyeh2020OutofSampleRL, title={Out-of-Sample Representation Learning for Multi-Relational Graphs}, author={Marjan Albooyeh and R. Goel and S. Kazemi}, journal={ArXiv}, year={2020}, volume={abs/2004.13230} }

Many important problems can be formulated as reasoning in multi-relational graphs. Representation learning has proved extremely effective for transductive reasoning, in which one needs to make new predictions for already observed entities. This is true for both attributed graphs (where each entity has an initial feature vector) and non-attributed graphs(where the only initial information derives from known relations with other entities). For out-of-sample reasoning, where one needs to make… CONTINUE READING

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Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction

- Computer Science, Mathematics
- 2020

- 1
- PDF

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