Order Interaction

Order interaction research focuses on understanding and modeling relationships between multiple variables beyond simple pairwise interactions, aiming to improve the accuracy and interpretability of complex systems modeling. Current research emphasizes developing novel neural network architectures, such as hypergraph neural networks and those leveraging simplicial complexes, along with algorithms like those based on Shapley values and Fourier analysis, to efficiently capture and quantify higher-order effects. This work has significant implications for various fields, including click-through rate prediction, protein structure prediction, and the interpretation of deep learning models, by enabling more accurate predictions and providing deeper insights into complex data relationships.