论文标题
可扩展的图形神经网络基于基于识别复杂网络中关键节点和链接的框架
Scalable Graph Neural Network-based framework for identifying critical nodes and links in Complex Networks
论文作者
论文摘要
在图中识别关键节点和链接是一项关键任务。这些节点/链接通常代表关键元素/通信链接，在系统性能中起关键作用。但是，文献中有关识别关键节点/链接的大多数方法基于一次探索图表的每个节点/链接的迭代方法，重复图中所有节点/链接。这种方法具有很高的计算复杂性，并且由此产生的分析也是网络特定的。为了克服这些挑战，本文提出了一个基于可扩展的图形神经网络（GNN）的框架，用于识别大型复杂网络中的关键节点/链接。所提出的框架定义了基于GNN的模型，该模型在节点/链接的小代表性子集中学习节点/链接临界度得分。可以使用适当的训练模型来预测大图中看不见的节点/链接的得分，因此可以确定最关键的节点/链接。通过在大规模合成和现实世界网络中预测节点/链接得分的预测来证明框架的可扩展性。所提出的方法在近似关键分数方面相当准确，并且比常规方法具有显着的计算优势。
Identifying critical nodes and links in graphs is a crucial task. These nodes/links typically represent critical elements/communication links that play a key role in a system's performance. However, a majority of the methods available in the literature on the identification of critical nodes/links are based on an iterative approach that explores each node/link of a graph at a time, repeating for all nodes/links in the graph. Such methods suffer from high computational complexity and the resulting analysis is also network-specific. To overcome these challenges, this article proposes a scalable and generic graph neural network (GNN) based framework for identifying critical nodes/links in large complex networks. The proposed framework defines a GNN based model that learns the node/link criticality score on a small representative subset of nodes/links. An appropriately trained model can be employed to predict the scores of unseen nodes/links in large graphs and consequently identify the most critical ones. The scalability of the framework is demonstrated through prediction of nodes/links scores in large scale synthetic and real-world networks. The proposed approach is fairly accurate in approximating the criticality scores and offers a significant computational advantage over conventional approaches.
