Admittedly, Graph Convolution Network (GCN) has achieved excellent results on graph datasets such as social networks, citation networks, etc. However, softmax used as the decision layer in these frameworks is generally optimized with thousands of iterations via gradient descent. Furthermore, due to ignoring the inner distribution of the graph nodes, the decision layer might lead to an unsatisfactory performance in semi-supervised learning with less label support. To address the referred issues, we propose a novel graph deep model with a non-gradient decision layer for graph mining. Firstly, manifold learning is unified with label local-structure preservation to capture the topological information of the nodes. Moreover, owing to the non-gradient property, closed-form solutions is achieved to be employed as the decision layer for GCN. Particularly, a joint optimization method is designed for this graph model, which extremely accelerates the convergence of the model. Finally, extensive experiments show that the proposed model has achieved state-of-the-art performance compared to the current models.

翻译：诚然,图集网络(GCN)在诸如社交网络、引证网络等图表数据集方面取得了极佳的成果。然而,这些框架中用作决策层的软分子通常通过梯度下降而以数千次迭代的形式得到优化。此外,由于忽略了图形节点的内部分布,决策层可能导致半监督学习表现不尽如人意,而标签支持较少。为了解决上述问题,我们提出了一个新的图表深层次模型,其中为图解开采提供了一个非梯度决定层。首先,多重学习与标签地方结构保护相统一,以捕捉节点的地形信息。此外,由于非梯度特性,将实现封闭式解决方案作为GCN的决策层。特别是,为这一图形模型设计了一个联合优化方法,大大加快了模型的趋同速度。最后,广泛的实验表明,与当前模型相比,拟议的模型取得了最先进的性能。