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Graph Diffusion-Embedding Networks
[article]
2018
arXiv
pre-print
We present a novel graph diffusion-embedding networks (GDEN) for graph structured data. GDEN is motivated by our closed-form formulation on regularized feature diffusion on graph. GDEN integrates both regularized feature diffusion and low-dimensional embedding simultaneously in a unified network model. Moreover, based on GDEN, we can naturally deal with structured data with multiple graph structures. Experiments on semi-supervised learning tasks on several benchmark datasets demonstrate the
arXiv:1810.00797v1
fatcat:66iqdsh5enhs3ogrtdda5ldfca