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.
UrbanGraphEmbeddings: Learning and Evaluating Spatially Grounded Multimodal Embeddings for Urban Science
National University of Singapore Department of Architecture Singapore；The Chinese University of Hong Kong Dept of Systems Eng. \& Eng. Mgmt. China；Singapore Management University School of Computing \& Info. Systems Singapore；National University of Singapore；Department of Architecture；The Chinese University of Hong Kong；Singapore Management University
Multimodal Deep Survival Analysis for Sinkhole Susceptibility
Huron High School（休伦高中）；University of Florida（佛罗里达大学）；Industrial and Systems Engineering（工业与系统工程系）；Forest, Fisheries, and Geomatics Sciences（森林、渔业与地理信息科学系）
Spiking Neural Networks for Mental Workload Classification with a Multimodal Approach
Institute of Neuroinformatics, University of Zurich（神经信息学研究所，苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Digital Society Initiative, University of Zurich（数字社会倡议，苏黎世大学）；Department of Geography（地理系）
