Graph Diffusion-Embedding Networks [article]

Bo Jiang, Doudou Lin, Jin Tang
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
more » ... er performance of the proposed GDEN when comparing with the traditional GCN models.
arXiv:1810.00797v1 fatcat:66iqdsh5enhs3ogrtdda5ldfca