In this paper, we investigate how class relationships are disrupted after large scale changes. We use graphs to represent different software versions and study changes to graph properties. We explore different combinatorial metrics to measure the extent of disruption after perfective maintenance activities. Our early results, on JHotDraw, demonstrate that combinatorial metrics can provide a good indicator to the degree to which relationships are disrupted or preserved across different versions.

Dynamic complex networks are used to model the evolving relationships between entities in widely varying fields of research such as epidemiology, ecology, sociology, and economics. In the study of complex networks, a network is said to have community structure if it divides naturally into groups of vertices with dense connections within groups and sparser connections between groups. Detecting the evolution of communities within dynamically changing networks is crucial to understanding complex systems. In this paper, we develop a fast community detection algorithm for real-time dynamic network data. Our method takes advantage of community information from previous time steps and thereby improves efficiency while maintaining the quality of community detection. Our experiments on citation-based networks show that the execution time improves as much as 30% (average 13%) over static methods.