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arXiv · 1810.00797

Graph Diffusion-Embedding Networks

Bo Jiang·Doudou Lin·Jin Tang
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Abstract

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 better performance of the proposed GDEN when comparing with the traditional GCN models.

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Bo Jiang, Doudou Lin, Jin Tang. 2018-10-01. Graph Diffusion-Embedding Networks. https://arxiv.org/abs/1810.00797

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