基于多任务学习图卷积模型的航空网络节点分类

doi:10.12305/j.issn.1001-506X.2022.07.31

Abstract

Abstract:

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.

Key words: airline network, graph neural network, node classification, multi task learning

CLC Number:

TP391.4

Cheng FAN, Buhong WANG, Jiwei TIAN. Node classification of airline network based on the graph convolution network model with multi-task learning[J]. Systems Engineering and Electronics, 2022, 44(7): 2341-2349.

Figures/Tables 10

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数据集	节点数	边数	最大度值	最大直径	节点类型
Brazil	131	1 038	80	4	4
Europe	399	5 995	202	5	4
USA	1 190	13 599	238	8	4

结果	预测为正常	预测为异常
实际为正常	True positive(TP)	False negative(FN)
实际为异常	False positive(FP)	True negative(TN)

数据集	模型	AUC	Micro-F1	Macro-F1
巴西	GCN	0.794	0.596	0.522
	GAT	0.783	0.623	0.543
	GraphSAGE	0.714	0.581	0.487
	DEMO-Net	0.806	0.652	0.572
	MTGCN	0.817	0.687	0.597
欧洲	GCN	0.593	0.537	0.473
	GAT	0.611	0.524	0.481
	GraphSAGE	0.558	0.487	0.452
	DEMO-Net	0.624	0.532	0.491
	MTGCN	0.656	0.548	0.503
美国	GCN	0.704	0.513	0.553
	GAT	0.713	0.534	0.561
	GraphSAGE	0.663	0.474	0.524
	DEMO-Net	0.724	0.557	0.568
	MTGCN	0.751	0.627	0.582

数据集	权重	迭代次数
数据集	权重	100	200	300	400	500	600	700	800	900	1 000
巴西	λ_n	0.776 6	0.987 1	1.280 0	1.236 7	1.198 20	1.148 6	1.128 3	1.111 9	1.125 5	1.139 2
巴西	λ_p	1.319 4	1.198 3	1.018 6	0.937 7	0.834 1	0.838 4	0.838 5	0.839 2	0.829 7	0.839 2
欧洲	λ_n	0.814 3	1.128 4	1.062 1	1.112 5	1.112 0	1.118 6	1.123 9	1.117 9	1.118 3	1.118 4
欧洲	λ_p	1.176 6	1.087 1	0.925 8	0.843 5	1.278 8	1.088 6	0.973 9	0.952 1	0.931 5	0.924 4
美国	λ_n	0.981 1	1.087 1	1.080 0	1.297 3	0.851 2	1.123 4	1.125 4	1.197 6	1.198 4	1.203 1
美国	λ_p	1.241 1	1.314 8	1.143 7	1.082 0	1.045 3	0.845 2	0.931 5	0.938 6	0.941 5	0.949 2

数据集	模型	准确率	Micro-F1	Macro-F1
巴西	with AT	0.817	0.687	0.597
巴西	without AT	0.792	0.656	0.582
欧洲	with AT	0.656	0.548	0.503
欧洲	without AT	0.631	0.523	0.491
美国	with AT	0.751	0.627	0.582
美国	without AT	0.734	0.608	0.571

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

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