Graph Diffusion-Embedding Networks
This work addresses graph-based semi-supervised learning, offering an incremental improvement over existing GCN methods.
The authors tackled the problem of learning from graph-structured data by proposing Graph Diffusion-Embedding Networks (GDEN), which integrates regularized feature diffusion and low-dimensional embedding in a unified model, showing better performance than traditional GCN models on semi-supervised learning tasks across benchmark datasets.
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.