Abstract Motivation Complex diseases are often caused and characterized by misregulation of multiple biological pathways. Differential network analysis aims to detect significant rewiring of biological network structures under different conditions and has become an important tool for understanding the molecular etiology of disease progression and therapeutic response. With few exceptions, most existing differential network analysis tools perform differential tests on separately learned network structures that are computationally expensive and prone to collapse when grouped samples are limited or less consistent. Results We previously developed an accurate differential network analysis method—differential dependency networks (DDN), that enables joint learning of common and rewired network structures under different conditions. We now introduce the DDN3.0 tool that improves this framework with three new and highly efficient algorithms, namely, unbiased model estimation with a weighted error measure applicable to imbalance sample groups, multiple acceleration strategies to improve learning efficiency, and data-driven determination of proper hyperparameters. The comparative experimental results obtained from both realistic simulations and case studies show that DDN3.0 can help biologists more accurately identify, in a study-specific and often unknown conserved regulatory circuitry, a network of significantly rewired molecular players potentially responsible for phenotypic transitions. Availability and implementation The Python package of DDN3.0 is freely available at https://github.com/cbil-vt/DDN3. A user’s guide and a vignette are provided at https://ddn-30.readthedocs.io/.