We propose GLAudio: Graph Learning on Audio representation of the node features and the connectivity structure. This novel architecture propagates the node features through the graph network according to the discrete wave equation and then employs a sequence learning architecture to learn the target node function from the audio wave signal. This leads to a new paradigm of learning on graph-structured data, in which information propagation and information processing are separated into two distinct steps. We theoretically characterize the expressivity of our model, introducing the notion of the receptive field of a vertex, and investigate our model's susceptibility to over-smoothing and over-squashing both theoretically as well as experimentally on various graph datasets.

翻译：我们提出GLAudio：基于音频表征的图节点特征与连接结构学习框架。该新颖架构依据离散波动方程在图形网络中传播节点特征，随后采用序列学习架构从音频波形信号中学习目标节点函数。这开创了图结构数据学习的新范式，将信息传播与信息处理分离为两个独立步骤。我们从理论上刻画了模型的表达能力，引入顶点感受野的概念，并通过理论分析与多图数据集的实验验证，探究了模型对过度平滑与过度挤压现象的敏感性。