Graph convolutional networks (GCNs) aim at extending deep learning to arbitrary irregular domains, namely graphs. Their success is highly dependent on how the topology of input graphs is defined and most of the existing GCN architectures rely on predefined or handcrafted graph structures. In this paper, we introduce a novel method that learns the topology (or connectivity) of input graphs as a part of GCN design. The main contribution of our method resides in building an orthogonal connectivity basis that optimally aggregates nodes, through their neighborhood, prior to achieve convolution. Our method also considers a stochasticity criterion which acts as a regularizer that makes the learned basis and the underlying GCNs lightweight while still being highly effective. Experiments conducted on the challenging task of skeleton-based hand-gesture recognition show the high effectiveness of the learned GCNs w.r.t. the related work.

翻译：图形革命网络(GCN)旨在将深度学习扩展至任意的不规则域,即图形。它们的成功在很大程度上取决于投入图的表层定义,现有的大部分GCN结构依赖预先定义或手工制作的图形结构。在本文中,我们引入了一种新的方法,学习输入图的表层学(或连接),作为GCN设计的一部分。我们的方法的主要贡献在于建立一个正方形连接基础,通过相邻地区,在革命前最优化地综合结点。我们的方法还考虑到一种随机性标准,它是一种常规化工具,既能提供学习基础,又能带来基本GCN的轻量,同时仍然非常有效。在基于骨骼的手进化识别这一具有挑战性的任务上进行的实验表明,学习的GCNs w.r.t.的相关工作具有很高的效力。