Network clustering algorithm for dynamic protein complex detection fused graph embedding

Network clustering for protein complex identification within protein interaction networks has long been a focal point for researchers in the field of machine learning and data mining. Although existing algorithms take into account the dynamic properties of protein complexes, they ignore the computational dynamics. This paper introduces a novel network clustering algorithm designed for dynamic protein complex detection. This approach integrates gene expression data and static network data to construct a dynamic network construction model and incorporates multiple data sources to formulate a node representation model. It devises a new dynamic clustering model that simulates the splitting, merging, and growth of protein complexes, providing a framework for dynamic network clustering. Experimental results demonstrate that the proposed algorithm outperforms dynamic clustering algorithms and traditional clustering algorithms.