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Most of the existing multi-relational network embedding methods, e.g.,TransE, are formulated to preserve pair-wise connectivity structures in thenetworks. With the observations that significant triangular connectivitystructures and parallelogram connectivity structures found in many realmulti-relational networks are often ignored and that a hard-constraint commonlyadopted by most of the network embedding methods is inaccurate by design, wepropose a novel representation learning model for multi-relational networkswhich can alleviate both fundamental limitations. Scalable learning algorithmsare derived using the stochastic gradient descent algorithm and negativesampling. Extensive experiments on real multi-relational network datasets ofWordNet and Freebase demonstrate the efficacy of the proposed model whencompared with the state-of-the-art embedding methods.

A Structural Representation Learning for Multi-relational Networks