Survey on Geospatial Network Representation Learning from a Causal Perspective: Advances, Challenges, and Prospects

A geospatial network refers to a graph whose nodes or edges are associated with geographic locations. As an important data type in GIS, it simultaneously models geographical entities and their relationships. With the development of deep representation learning, leveraging deep models to automatically extract representations of geospatial networks has become the mainstream paradigm. The existing representation learning paradigm adopts the correlations observed in data as learnable signals but is unable to address the challenges posed by certain characteristics of geospatial networks. Causal representation learning aims to introduce causal knowledge as a learning constraint within the existing paradigm, offering potential solutions to such challenges. This article discusses the necessity of introducing causal assumptions for geospatial network representation and summarizes the current progress in causal representation learning for geospatial networks. To better distill the key concepts and fundamental issues of causal representation learning for geospatial networks, this article uses one of the most frequently used frameworks, the structural causal model, as the common thread, formalizing causal learning in geospatial networks into three sub-tasks. A unified framework for geospatial network representation learning is constructed, based on which the significance of introducing causal learning strategies is summarized. Additionally, this article categorizes causal relationships in geospatial networks into spatial causality and temporal causality, providing a comprehensive review and analysis of existing studies in each area. The challenges in current research are summarized, and future research directions are explored. Furthermore, this article abstracts a general framework for geospatial network representation learning, offering a reference skeleton for introducing other assumptions into geospatial network research.