In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs. Our approach introduces a novel framework for contrastive learning, a widely prevalent technique for unsupervised representation learning. A crucial step in contrastive learning is the creation of 'augmented' graphs from the input graphs. Though different from the original graphs, these augmented graphs retain the original graph's structural characteristics. Here, we propose a unique method for generating these augmented graphs by leveraging the control properties of networks. The core concept revolves around perturbing the original graph to create a new one while preserving the controllability properties specific to networks and graphs. Compared to the existing methods, we demonstrate that this innovative approach enhances the effectiveness of contrastive learning frameworks, leading to superior results regarding the accuracy of the classification tasks. The key innovation lies in our ability to decode the network structure using these control properties, opening new avenues for unsupervised graph representation learning.

翻译：本文研究了通过利用图上动态网络的控制属性进行无监督图表示学习的问题。我们提出了一种新颖的对比学习框架——对比学习是无监督表示学习中广泛采用的技术。对比学习的关键步骤是从输入图创建"增强"图，这些增强图虽与原始图不同，但保留了原始图的结构特征。本文提出了一种独特方法，通过利用网络的控制属性来生成这些增强图。其核心思想是在保持网络和图特有可控性的前提下扰动原始图以生成新图。与现有方法相比，我们证明这种创新方法能够提升对比学习框架的有效性，在分类任务准确率方面取得更优结果。关键创新在于我们能够利用这些控制属性解码网络结构，为无监督图表示学习开辟了新途径。