超图
利用
计算机科学
数据挖掘
通信源
人工神经网络
特征(语言学)
关系(数据库)
构造(python库)
图形
理论计算机科学
人工智能
机器学习
数学
计算机网络
哲学
离散数学
语言学
计算机安全
作者
Lei Zhang,Xingyu Wu,Yong Liu,Xin Zhou,Yiming Cao,Yonghui Xu,Lizhen Cui,Miao Chen
标识
DOI:10.1016/j.eswa.2023.121740
摘要
Estimated Time of Arrival (ETA) for packages plays an essential role in intelligent logistics. As a classic ETA method, Origin–Destination-based (OD-based) ETA predicts the delivery time only based on the attributes (i.e., sender address, receiver address, seller, and payment time) of packages under the condition that the delivery route is unavailable. However, existing OD-based methods only exploit attributes associated with an individual order, which fails to model the higher-order interactions within orders and attributes, and fail to sufficiently exploit the graph-structure knowledge (i.e., relation of orders and attributes) and feature-based knowledge (i.e., statistical properties) of orders simultaneously, resulting in inaccurate predictions. In this paper, we propose a novel Heterogeneous HyperGraph Neural Network (H2GNN) for estimating package arrival time. Specifically, to better capture the high-order interactions within orders and attributes, we construct an order heterogeneous hypergraph that utilizes hyperedges to represent orders and nodes to represent order attributes. Besides, we extend the hypergraph learning for large-scale e-commerce data by Hyper-GraphSAGE. Overall, H2GNN can provide informatively representations of packages while preserving both structure-based knowledge learned by hypergraph and feature-based knowledge captured by Transformer. Experimental results on large-scale Alibaba logistics data demonstrate the superior performance of H2GNN compared to the baselines.
科研通智能强力驱动
Strongly Powered by AbleSci AI