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arXiv · 1805.06197

A Structural Representation Learning for Multi-relational Networks

Xin Li·Huiting Hong·Lin Liu·William K. Cheung
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Abstract

Most of the existing multi-relational network embedding methods, e.g., TransE, are formulated to preserve pair-wise connectivity structures in the networks. With the observations that significant triangular connectivity structures and parallelogram connectivity structures found in many real multi-relational networks are often ignored and that a hard-constraint commonly adopted by most of the network embedding methods is inaccurate by design, we propose a novel representation learning model for multi-relational networks which can alleviate both fundamental limitations. Scalable learning algorithms are derived using the stochastic gradient descent algorithm and negative sampling. Extensive experiments on real multi-relational network datasets of WordNet and Freebase demonstrate the efficacy of the proposed model when compared with the state-of-the-art embedding methods.

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Xin Li, Huiting Hong, Lin Liu, William K. Cheung. 2018-05-16. A Structural Representation Learning for Multi-relational Networks. https://arxiv.org/abs/1805.06197

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