论文标题
图形的环储存神经网络
Ring Reservoir Neural Networks for Graphs
论文作者
论文摘要
如今，图形的机器学习是一个合并相关性的研究主题。该领域的常见方法通常诉诸复杂的深度神经网络体系结构和要求的培训算法，从而强调了对更有效的解决方案的需求。储层计算（RC）模型类别可以在这种情况下发挥重要作用，从而通过未经训练的递归体系结构来开发富有成果的图形嵌入。在本文中，我们研究了图形RC神经网络的设计策略的进行性简化。我们的核心建议是基于塑造隐藏神经元的组织以遵循环形拓扑。图形分类任务的实验结果表明，环形架构体系结构可实现特别有效的网络配置，在预测性能方面显示出一致的优势。
Machine Learning for graphs is nowadays a research topic of consolidated relevance. Common approaches in the field typically resort to complex deep neural network architectures and demanding training algorithms, highlighting the need for more efficient solutions. The class of Reservoir Computing (RC) models can play an important role in this context, enabling to develop fruitful graph embeddings through untrained recursive architectures. In this paper, we study progressive simplifications to the design strategy of RC neural networks for graphs. Our core proposal is based on shaping the organization of the hidden neurons to follow a ring topology. Experimental results on graph classification tasks indicate that ring-reservoirs architectures enable particularly effective network configurations, showing consistent advantages in terms of predictive performance.
