Background: Identifying functional modules (FM) in Protein-Protein Interaction
(PPI) networks is essential for understanding the organization and evolution of cellular
systems. Most current functional module discovery algorithms merely focus on the static PPI
network. However, PPI network is dynamic over time and varies under different conditions.
Objective: Therefore, discovering functional modules in dynamic PPI networks (DPN) is
crucial. In this paper, functional module is defined as the union of a time-line of evolutionary
step-modules. A novel StableCore and Adaptive Incremental Algorithm (SCAIA) is
developed to discover functional modules in DPN.
Method: The SCAIA first detects static step-modules of the first subnetwork and adaptively
updates the modular structure of other subnetworks, and then identifies functional modules
and their evolutionary trends based on the extracted step-modules of each subnetwork.
Results: Extensive results show SCAIA achieves very satisfactory Precision, F-measure and Pvalue
results among the seven functional module discovery algorithms compared in this study.
Conclusion: SCAIA performs significantly better than seven methods on discovering
accurate and stable functional modules. SCAIA can also track the evolutionary process of
functional modules over time, providing insights into the underlying behavior of functional
modules for future biological studies.