2 research outputs found
struc2gauss: Structure Preserving Network Embedding via Gaussian Embedding
Network embedding (NE) is playing a principal role in network mining, due to
its ability to map nodes into efficient low-dimensional embedding vectors.
However, two major limitations exist in state-of-the-art NE methods: structure
preservation and uncertainty modeling. Almost all previous methods represent a
node into a point in space and focus on the local structural information, i.e.,
neighborhood information. However, neighborhood information does not capture
the global structural information and point vector representation fails in
modeling the uncertainty of node representations. In this paper, we propose a
new NE framework, struc2gauss, which learns node representations in the space
of Gaussian distributions and performs network embedding based on global
structural information. struc2gauss first employs a given node similarity
metric to measure the global structural information, then generates structural
context for nodes and finally learns node representations via Gaussian
embedding. Different structural similarity measures of networks and energy
functions of Gaussian embedding are investigated. Experiments conducted on both
synthetic and real-world data sets demonstrate that struc2gauss effectively
captures the global structural information while state-of-the-art network
embedding methods fails to, outperforms other methods on the structure-based
clustering task and provides more information on uncertainties of node
representations