The ability for people and organizations to connect in the digital age has allowed the growth of networks that cover an increasing proportion of human interactions. The research community investigating networks asks a range of questions such as which participants are most central, and which community label to apply to each member. This paper deals with the question on how to label nodes based on the features (attributes) they contain, and then how to model the changes in the label assignments based on the influence they produce and receive in their networked neighborhood. The methodological approach applies the simple graph convolutional neural network in a novel setting. Primarily that it can be used not only for label classification, but also for modeling the spread of the influence of nodes in the neighborhoods based on the length of the walks considered. This is done by noticing a common feature in the formulations in methods that describe information diffusion which rely upon adjacency matrix powers and that of graph neural networks. Examples are provided to demonstrate the ability for this model to aggregate feature information from nodes based on a parameter regulating the range of node influence which can simulate a process of exchanges in a manner which bypasses computationally intensive stochastic simulations.

中文翻译：

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使用简单图卷积神经网络作为模型来模拟网络中的影响扩散

人和组织在数字时代进行连接的能力使覆盖人类交互比例越来越高的网络得以发展。研究社区调查网络提出了一系列问题，例如哪些参与者最重要，以及哪个社区标签适用于每个成员。本文涉及以下问题：如何根据节点所包含的特征（属性）来标记节点，然后如何根据节点在网络邻居中产生和接收的影响来对标签分配中的变化进行建模。该方法论方法在简单的情况下应用了简单的图卷积神经网络。主要是它不仅可以用于标签分类，而且还可以根据所考虑的步行时间来建模节点在邻里中的影响力分布。这是通过在描述信息扩散的方法中注意到配方中的一个共同特征来完成的，这些信息依赖于邻接矩阵的幂和图神经网络的幂。提供了一些示例来说明此模型基于调节节点影响范围的参数来汇总来自节点的特征信息的能力，该参数可以绕过计算密集型随机模拟的方式来模拟交换过程。