Structure-guided attributed network embedding with “centroid” enhancement

Attributed network embedding aims at learning low-dimensional network representations in terms of both network structure and attribute information. Most existing methods deal with network structure and attributes separately and combine them in particular ways, which weaken the affinity between structure and attributes and thus lead to suboptimal performance. Moreover, some methods focus solely on local or global network structure, without fully utilizing the structure information underling the network. To address these limitations, we propose structure-guided attributed network embedding with “centroid” enhancement, an unsupervised approach to embed network structure and attribute information comprehensively and seamlessly. Specifically, we regard the neighborhood of each node as a “cluster” and calculate a “centroid” for it through graph convolutional network. We design a “centroid”-based triplet regularizer to impose a gap constraint inspired by K-means. A “centroid”-augment skip-gram model is utilized to deal with high-order proximity. By jointly optimizing the two objectives, the learned representation can preserve both local-global network structure and attribute information. Throughout the model, we exploit network structure to guide the aggregation of attributes, and thus effectively captures the affinity between them. Experimental results on eight real-world datasets demonstrate the superiority of our model over the state-of-the-art methods.