Over the recent years, Graph Neural Networks have become increasingly popular in network analytic and beyond. With that, their architecture noticeable diverges from the classical multi-layered hierarchical organization of the traditional neural networks. At the same time, many conventional approaches in network science efficiently utilize the hierarchical approaches to account for the hierarchical organization of the networks, and recent works emphasize their critical importance. This paper aims to connect the dots between the traditional Neural Network and the Graph Neural Network architectures as well as the network science approaches, harnessing the power of the hierarchical network organization. A Hierarchical Graph Neural Network architecture is proposed, supplementing the original input network layer with the hierarchy of auxiliary network layers and organizing the computational scheme updating the node features through both - horizontal network connections within each layer as well as the vertical connection between the layers. It enables simultaneous learning of the individual node features along with the aggregated network features at variable resolution and uses them to improve the convergence and stability of the individual node feature learning. The proposed Hierarchical Graph Neural network architecture is successfully evaluated on the network embedding and modeling as well as network classification, node labeling, and community tasks and demonstrates increased efficiency in those.
翻译:近年来,图形神经网络在网络分析中和网络分析中越来越受欢迎,因此,其结构与传统神经网络传统的多层次结构结构明显不同。与此同时,网络科学中的许多常规方法有效地利用等级方法来核算网络的等级组织,以及最近的工作强调其至关重要性。本文件旨在将传统神经网络和图形神经网络结构以及网络科学方法之间的点连接起来,利用层次网络组织的力量。提议了一个等级结构,用辅助网络层的等级补充原始输入网络层,并组织计算方案,通过各层的横向网络连接以及各层之间的纵向连接更新节点特征。它使得能够同时学习个别节点特征以及各种分辨率的汇总网络特征,并利用这些特征来改进个人节点特征学习的趋同和稳定性。拟议中的高级图形神经网络结构正在成功地评价网络嵌入和建模的网络结构,作为网络分类、不设标签和显示网络效率的提高以及社区任务。