基于图卷积的动态航线网络链路预测模型研究OACSTPCD
Research on Dynamic Route Network Link Prediction Model Based on Graph Convolution
为了克服航线网络传统链路预测模型的局限性,考虑网络拓扑特征和时间特性,提出一种基于图卷积网络(Graph Convolution Network,GCN)的动态航线网络链路预测模型.通过图卷积、图池化操作聚合节点及邻域信息,更新节点特征表示,识别节点间潜在联系.实验表明,GCN模型相较于传统链路预测模型具有更高的预测精度、预测效率和性能稳定性,是进行动态航线网络链路预测的一种有效工具.
In order to overcome the limitations of traditional link prediction models in route networks,a dynamic route net-work link prediction model based on graph convolutional network(GCN)is proposed,taking into account network topology and time characteristics.This paper aggregates node and neighborhood information through graph convolution and graph pooling oper-ates,updates node feature representations,and identifies potential connections between nodes.Experiments have shown that the GCN model has higher prediction accuracy,efficiency,and performance stability compared to traditional link prediction models,making it an effective tool for dynamic route network link prediction.
赵联政;张培文;黎砾丹
中国民用航空飞行学院机场学院 广汉 618307中国民用航空飞行学院经济与管理学院 广汉 618307成都双流国际机场股份有限公司 成都 610000
航空运输链路预测图卷积网络动态航线网络航线预测
air transportationlink predictiongraph convolution networkdynamic route networkroute prediction
《舰船电子工程》 2024 (005)
36-40 / 5
国家自然科学基金项目(编号:U2033213,U1733127);中央高校基本科研业务费专项资金项目(编号:J2022-041)资助.
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