基于多任务学习图卷积模型的航空网络节点分类

准确识别航空网络关键节点, 做好针对性防护, 对于保证航空网络正常运行至关重要。传统的方法, 如基于复杂网络中心性指标的方法, 或基于机器学习的算法, 只单一考虑网络结构或节点特征来评价节点的重要性。然而评价节点的重要性应该同时考虑网络结构特征和节点特征。为解决上述问题, 本文提出了一种名为多任务图卷积网络(multi tasks graph convolution network, MTGCN)航空网络节点分类模型, 该模型在图卷积网络的基础上, 引入多任务学习及自适应加权策略, 将“节点—节点相关性”作为辅助任务加入模型的训练过程中, 并根据训练情况自适应分配各任务权重。3个不同规模的航空网络数据集中的仿真实验表明本文所提模型的性能优于现有的图卷积模型, 为图卷积在航空网络节点分类方向的应用提供了思路。

Node classification of airline network based on the graph convolution network model with multi-task learning

Accurate identification of key nodes in airline networks and targeted protection are essential to ensure the normal operation of airline networks. Traditional methods, such as those based on complex network centrality metrics or machine learning-based algorithms, only consider the network structure or node features to evaluate the importance of nodes. However, the importance of nodes should be evaluated by considering both the network structure and node features. To solve this problem, this paper proposes a node classification model named multi tasks graph convolution network (MTGCN), which introduces multi-task learning and adaptive weighting strategy into graph convolution network, adds node-node correlation as an auxiliary task to the training process of the model, and adaptively assigns weights to each task according to the training condition. The results show that the proposed model outperforms the existing graph convolutional model and provides an idea for the application of graph convolution network in aviation networks node classification.