基于图神经网络的复杂网络关键节点识别方法

A Graph Neural Network-Based Method for Vital NodeIdentification in Complex Networks

在复杂网络中,识别关键节点对于提高网络的可靠性、保证网络的安全有效运行具有重要意义。然而,传统的中心性方法是片面的、不准确的,中心性方法在不同网络效果不同;同时,许多节点具有相同的中心性值,难以区分节点之间的差异。为更好地识别关键节点、提高节点分辨率,运用图结构学习的思想,通过图神经网络编码节点、拟合节点影响力,传播场域学习器计算网络节点传播场域,并使用节点的度作为模型观测值训练模型。最后,利用易感-感染(susceptibility-infection,SI)模型进行传播模拟,使用肯德尔相关系数衡量节点排序单调性。实验结果表明,在4个现实社会网络上,传播场域方法在以SI传播模型衡量排序性能和以肯德尔相关系数衡量排序结果单调性方面具有更好的性能,提高了关键节点识别的准确性和网络节点排序的鲁棒性。

In complex networks,identifying vital nodes is important to improve the reliability of the network and ensure its safe and effective operation.However,traditional centrality methods are one-sided and inaccurate,and centrality methods work differently in different networks.At once,many nodes have the same centrality value,making it difficult to distinguish differences between nodes.In order to better identify vital nodes and improve node resolution,inspired by graph structure learning,graph neural network was adopted to encode nodes and fit node influence,the propagation field learner was used to compute the propagation field of network nodes,and the degrees of node were observed as labels to train the model.Finally,the SI(susceptibility-infection)model was used for propagation simulation,and kendall s correlation coefficient was used as a measure of node ordering monotonicity.The results in real-world networks show that the algorithm has better better performance in measuring the sorting performance with the SI propagation model and the monotonicity of the sorting results with the kendall correlation coefficient,which improves the accuracy of vital nodes identification and the robustness of network node sorting.