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Network embedding (NE) is playing a principal role in network mining, due toits ability to map nodes into efficient low-dimensional embedding vectors.However, two major limitations exist in state-of-the-art NE methods: rolepreservation and uncertainty modeling. Almost all previous methods represent anode into a point in space and focus on local structural information, i.e.,neighborhood information. However, neighborhood information does not captureglobal structural information and point vector representation fails in modelingthe uncertainty of node representations. In this paper, we propose a new NEframework, struc2gauss, which learns node representations in the space ofGaussian distributions and performs network embedding based on globalstructural information. struc2gauss first employs a given node similaritymetric to measure the global structural information, then generates structuralcontext for nodes and finally learns node representations via Gaussianembedding. Different structural similarity measures of networks and energyfunctions of Gaussian embedding are investigated. Experiments conducted onreal-world networks demonstrate that struc2gauss effectively captures globalstructural information while state-of-the-art network embedding methods failto, outperforms other methods on the structure-based clustering andclassification task and provides more information on uncertainties of noderepresentations.

struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding