提出多模态空间图网络，提升嵌入式网络的表示精度。
Generic Multimodal Spatially Graph Network for Spatially Embedded Network Representation Learning
- 融合节点与边的多维空间特征，学习连接模式
- 在电力网中预测边存在性，准确率提升37.1%
- 适合处理自然与人工嵌入式网络，如河流、电网
空间嵌入网络（SENs）是一类拓扑受其空间环境约束的复杂图结构，其图表示同时受节点与边的空间特征影响。准确建模此类网络的结构与特征是多种图任务的基础。本文提出通用多模态空间图卷积网络（GMu-SGCN），可利用多模态节点与边特征学习节点连接模式。在河流网络（自然形成）与电力网络（人工构建）两个数据集上进行评估，二者均受空间环境与自然不确定性显著影响。实验表明，在电力网络测试中，相比仅使用节点位置特征的GraphSAGE模型，本模型在边存在性预测任务上准确率提升37.1%。结果验证了多维空间特征对空间嵌入网络表征的重要性。
原文摘要 · Abstract (English)
Spatially embedded networks (SENs) represent a special type of complex graph, whose topologies are constrained by the networks' embedded spatial environments. The graph representation of such networks is thereby influenced by the embedded spatial features of both nodes and edges. Accurate network representation of the graph structure and graph features is a fundamental task for various graph-related tasks. In this study, a Generic Multimodal Spatially Graph Convolutional Network (GMu-SGCN) is developed for efficient representation of spatially embedded networks. The developed GMu-SGCN model has the ability to learn the node connection pattern via multimodal node and edge features. In order to evaluate the developed model, a river network dataset and a power network dataset have been used as test beds. The river network represents the naturally developed SENs, whereas the power network represents a man-made network. Both types of networks are heavily constrained by the spatial environments and uncertainties from nature. Comprehensive evaluation analysis shows the developed GMu-SGCN can improve accuracy of the edge existence prediction task by 37.1\% compared to a GraphSAGE model which only considers the node's position feature in a power network test bed. Our model demonstrates the importance of considering the multidimensional spatial feature for spatially embedded network representation.
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