Motif-Based Linearizing Graph Transformer for Web API Recommendation.

The rapid growth of Web services, often in the form of APIs, has made it challenging for developers to choose the right ones. While graph neural network models have improved recommendations, they struggle with complex and long-distance node relationships, limiting their effectiveness in large-scale API recommendations. This paper introduces a motif-based linearizing graph Transformer with a novel positional encoding method, integrating global and local node relationships. By leveraging motif information, we achieve linear time complexity. Experiments show our model outperforms state-of-the-art methods, highlighting the potential of global attention in Web API recommendations. The codes are available at .