基于图卷积神经网络的位置语义推断  

Location Semantics Inference with Graph Convolutional Networks

作　　者：吴睿智[1] 朱大勇[1] 王春雨 秦科[1] WU Rui-zhi;ZHU Da-yong;WANG Chun-yu;QIN Ke(School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu,611731)

机构地区：[1]电子科技大学计算机科学与工程学院,成都611731

出　　处：《电子科技大学学报》2020年第5期739-744,共6页Journal of University of Electronic Science and Technology of China

基　　金：国家重点研发计划(2018YFC0807501);四川省科技厅项目(2018JY0605,2018GZDZX0048)。

摘　　要：挖掘位置社交网络(LBSNs)中的签到数据背后所蕴藏的信息是城市计算、智慧城市的重要研究方向,其中一个关键的任务是推断位置语义。位置语义因其在位置检索、位置推荐、数据预处理等领域的广泛应用而受到越来越多的关注。现有的推断方法倾向于手工提取位置的时空特征或用户签到活动的时空模式训练分类器进而推断位置语义。然而,提取有价值的时空模式或时空特征是一项困难的任务。该文提出一种新的基于图卷积神经网络的位置语义推理模型(SI-GCN)。SIGCN利用node2vec和变分自编码器来学习位置的空间和时间特征。构建用户−位置访问二部图,利用图卷积神经网络来捕获用户签到活动中的高阶信息。此外,SI-GCN引入自注意力机制区分用户−位置访问二部图中不同邻居节点的贡献。SIGCN在两个真实签到数据集上的实验表明,SI-GCN比现有3种算法具有更好的推断性能。Data mining on check-in data inlocation based social networks(LBSNs)is an important research direction of urban computing and smart city,and a critical task is to infer location semantic.The study of location semantics has attracted increasing attention in diverse fields due to its wide applications such as location retrieval,location recommendation,data preprocessing and so on.Established inference approaches tend to manually discover the spatiotemporal pattern of unique location as features for training classifiers.However,extracting valuable spatiotemporal patterns or features is a non-trivial task.In this paper,we propose a novel location semantic inference with graph convolutional networks(SI-GCN).We introduce node2vec and variational autoencoder to learn spatial and temporal features of location,respectively.Furthermore,we leverage graph convolutional networks to capture high order relations in user’s check-in activity with building a user-location bipartite network.And leveraging self-attention mechanism is allowed to distinguish contributions of the different nodes.Extensive experiments on several real-world check-in data sets show that our proposed framework outperforms than three state-of-art algorithms.

关 键 词：数据挖掘 图卷积神经网络 位置社交网络 自注意力机制 语义推断 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程]