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Textual network embeddings aim to learn a low-dimensional representation forevery node in the network so that both the structural and textual informationfrom the networks can be well preserved in the representations. Traditionally,the structural and textual embeddings were learned by models that rarely takethe mutual influences between them into account. In this paper, a deep neuralarchitecture is proposed to effectively fuse the two kinds of informations intoone representation. The novelties of the proposed architecture are manifestedin the aspects of a newly defined objective function, the complementaryinformation fusion method for structural and textual features, and the mutualgate mechanism for textual feature extraction. Experimental results show thatthe proposed model outperforms the comparing methods on all three datasets.

A Deep Neural Information Fusion Architecture for Textual Network Embeddings