Machine Learning on Dynamic Graphs: A Survey on Applications
It provides a comprehensive overview for researchers and practitioners interested in expanding the use of dynamic graph learning in various real-world and scientific contexts, but it is incremental as it focuses on reviewing existing applications rather than introducing new methods.
This survey paper reviewed lesser-explored applications of dynamic graph learning, revealing its potential to address challenges across diverse domains, including those with limited prior association to the field.
Dynamic graph learning has gained significant attention as it offers a powerful means to model intricate interactions among entities across various real-world and scientific domains. Notably, graphs serve as effective representations for diverse networks such as transportation, brain, social, and internet networks. Furthermore, the rapid advancements in machine learning have expanded the scope of dynamic graph applications beyond the aforementioned domains. In this paper, we present a review of lesser-explored applications of dynamic graph learning. This study revealed the potential of machine learning on dynamic graphs in addressing challenges across diverse domains, including those with limited levels of association with the field.