SDW2vec: learning structural representations of nodes in weighted networks

Abstract Recent advances in machine learning have prompted researchers to integrate complex network structures into computational frameworks to improve inferential capabilities. Node embedding has become a promising technique in this area. However, challenges persist in accurately representing the structural characteristics of nodes in weighted networks. In this study, we propose SDW2vec, which learns the embeddings of nodes in weighted networks while preserving structural properties. Our proposed methodology addresses these challenges through a multi-scale comparison of link weights among adjacent nodes up to a predefined hop count. This approach facilitates the calculation of distances between nodes’ structural configurations across multiple scales. We subsequently construct weighted multi-layer graphs based on these distance measurements, apply random walks to generate node sequences, and learn the embedding representations using the Skip-gram model. The efficacy of our methodology is validated through both the interpretability of embedding representations in controlled network environments and the structural reproducibility demonstrated in real-world networks.