CAREER: Spatial Network Deep Generative Modeling, Transformation, and Interpretation

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As we enter the modern big data era, spatial data and network data are popular types of high-dimensional data with continuous and discrete properties, respectively. Spanning these two data types, spatial networks represent a crucial data structure where the nodes and edges are embedded in a geometric space. Nowadays, spatial network data is becoming increasingly popular and important, ranging from micro-scale (e.g., protein structures), to middle-scale (e.g., biological neural networks), to macro-scale (e.g., mobility networks). The modeling of spatial networks is extremely difficult due to the significant challenges involved, including: 1) incompatibility between the treatments for continuous spatial properties and discrete network properties, 2) the close interactions between spatial and network topologies, and 3) their extremely high dimensionality. These challenges echo numerous unsolved critical issues in the real world such as modeling and understanding the "protein structure folding process" and "mental disease mechanisms in brain networks". Until now, there has been a significant gap between our lack of powerful models and the extremely complex research issues involved in modeling the generation of spatial networks. To fill this gap, this project focuses on developing a transformative framework for spatial network generative modeling, which can automatically learn the underlying complex generation process from massive spatial network datasets.This project generalizes existing generative models of spatial networks into deep and expressive architectures. The developed framework aims at: 1) automatically learning new generation and transformation process of spatial networks, 2) embedding user-specified principles to constrain and regularize the generated spatial networks, and 3) pursuing the model interpretability and automatically distill new understandable principles of spatial network process. The research activities are conducted along the following themes: i) novel spatial and spectral graph decoders for large spatial networks, ii) deep generative modeling and optimization with spatial and topological constraints and regularization, iii) a variety of novel spatial- and spectral- graph transformation strategies, and iv) a novel system for interacting the predefined and distilled principles between human and models. The techniques developed in this project aim at benefiting various social and natural science domains by enabling efficient and accurate discovery and synthesis of complex spatial network behavior. The success of this project can benefit crucial domains including medicine design, mental disease early diagnoses, and disaster management. Core products of this project, including publications, software, and datasets, are published in various websites with active user support, in order to largely benefit the research communities and the society. The proposed unified framework is also used for teaching spatial and network data mining concepts, as well as providing graduate and undergraduate students with new courses, research, and internship opportunities. This project actively includes underrepresented students and outreach to local high schools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.