Interaction Prediction

Interaction prediction focuses on forecasting future relationships between entities, whether they are molecules, humans and objects, vehicles in traffic, or nodes in a network. Current research emphasizes developing sophisticated models, including graph neural networks, transformers, and temporal point processes, to capture complex, higher-order interactions and temporal dynamics, often incorporating uncertainty quantification and multi-modal data. These advancements have significant implications for diverse fields, improving drug discovery, robotic manipulation, autonomous vehicle navigation, and the understanding of complex biological and social systems. The ultimate goal is to create more accurate and robust predictive models that can handle the inherent complexities and uncertainties of real-world interactions.