Pairwise Interaction

Pairwise interaction analysis focuses on understanding the relationships between pairs of entities within complex systems, aiming to uncover causal dependencies and predict future interactions. Current research emphasizes developing efficient algorithms and model architectures, such as graph neural networks and hypergraph contrastive learning, to analyze diverse data types (e.g., time series, images, network structures) and extract higher-order interactions beyond simple pairwise relationships. These advancements have significant implications across various fields, improving the accuracy of predictions in areas like chemical engineering (thermodynamic property prediction), biology (gene interaction discovery), and social sciences (social hierarchy modeling), as well as enabling more efficient machine learning models through structured network pruning.