Meta Path

Meta-paths, sequences of node and edge types in heterogeneous graphs, are crucial for extracting meaningful information and improving performance in various graph-based machine learning tasks. Current research focuses on automatically learning informative meta-paths, often integrating them into graph neural networks (GNNs) through techniques like contrastive learning and reinforcement learning, to overcome limitations of manually defined paths and enhance model accuracy and efficiency. This work has significant implications for diverse applications, including drug discovery, recommendation systems, and rumor detection, by enabling more effective analysis of complex, multi-relational data.