Abstract: Node importance ranking is a critical task in graph analysis, as it plays a crucial role in identifying and prioritizing important nodes within a graph. Graph neural networks (GNNs) serve as an effective framework that leverages deep learning to directly comprehend the structural data of graphs, enabling comprehensive understanding of the internal patterns and deeper semantic features associated with nodes and edges. In the context of node importance ranking, GNNs can effectively harness graph structure information and node features to assess the significance of individual nodes. Compared with traditional node ranking methods, GNNs are better equipped to handle the diverse and intricate nature of graph structural data, capturing complex associations and semantic information between nodes while autonomously learning representations for node features. This reduces reliance on manual feature engineering, thereby enhancing accuracy in node importance ranking tasks. Consequently, approaches based on graph neural networks have emerged as the predominant direction for research into node importance. On this basis, this paper provides a classification of recent advancements in node ranking methods utilizing graph neural networks. This paper begins by revisiting core concepts related to node ranking, graph neural networks, and classical metrics for assessing node importance. It then summarizes recent developments in methods for evaluating node importance using graph neural networks. These techniques are categorized into four groups based on fundamental graph neural networks and their variants: basic GNNs, graph convolutional neural networks (GCNs), graph attention networks (GATs), and graph autoencoders (GAEs). Additionally, this paper analyzes the performance of these methods across various application domains, such as social networks, traffic networks, and knowledge graphs. Finally, it offers a comprehensive overview of existing research by analyzing time complexity along with advantages, limitations, and performance characteristics of current methodologies. Furthermore, it discusses future research directions based on identified shortcomings.

Key words: node importance, node ranking, graph neural network, representation learning

摘要： 节点重要性排序作为一项关键的图数据分析任务，对于识别和排序图中的重要节点至关重要。图神经网络（GNN）作为一种利用深度学习直接对图结构数据进行学习的框架，能够充分学习图结构数据中的节点和边的内在规律及更深层次的语义特征。在节点重要性排序任务中，GNN能够充分利用图结构信息和节点特征进行节点重要性的评估。相比于传统的节点排序方法，GNN可以更好地处理图结构数据的多样性和复杂性，捕捉节点间的复杂关联和语义信息，并自动学习节点特征表示，减少手工特征工程的偏差，提升节点重要性排序任务的准确性。因此，基于图神经网络的方法已成为节点重要性研究的主流方向。对近年来图神经网络的节点排序方法进行分类和综述。梳理了节点排序、图神经网络及经典节点重要性度量指标的核心概念。全面总结了基于图神经网络的节点重要性方法的最新进展，并根据基础图神经网络及其衍生的变体，将节点重要性排序方法分为基础图神经网络、图卷积神经网络、图注意力网络和图自编码器四类。同时，分析这些方法在社交网络、交通网络和知识网络等下游任务中的性能表现。对现有研究进行全面总结，分析现有方法的时间复杂度、优点、局限性和性能，并根据现有研究的不足讨论未来的研究方向。

关键词: 节点重要性, 节点排序, 图神经网络, 表示学习