| Summary: | 碩士 === 國立臺灣大學 === 資訊網路與多媒體研究所 === 105 === Multi-relational networks are ubiquitous in real world. It is, however, difficult to be analyzed due to the complex structure of the network. A plausible approach to analyze such network is to embed the entity information as an informative feature vector. However, present embedding methods either consider only single-relational information, or neglect the importance of structural information. In addition, some of them require fine-tuning of hyperparameters, which might not be feasible for an unsupervised embedding generation task.
In this work we propose MUSE, a Multi-relational Unsupervised link-Structure preserving Embeddings method, which learns the representations for each node and relation by maximizing the likelihood of observations on the given network. Additional node attributes are also preserved under our design. Besides, MUSE features less sensitive hyperparameters and scalablility by edge-sampling strategy. The extensive experiments on various real-world applications also demonstrate the effectiveness and robustness of our model.
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