Information Propagation

Information propagation studies how information spreads through networks, aiming to understand and optimize this process across various domains. Current research focuses on improving the efficiency and accuracy of information propagation using graph neural networks (GNNs), often incorporating techniques like attention mechanisms, bidirectional information flow, and hierarchical structures to capture complex relationships and handle long-range dependencies. These advancements have significant implications for diverse fields, including social network analysis (e.g., rumor detection, influence maximization), knowledge graph reasoning, and improving the performance of deep learning models by enhancing information flow within their architectures.

Papers