Computational discovery in evolving complex networks

The field of study in evolving complex networks has more and more researchers working using various methods. We designed and developed a computational discovery methodology to study these evolving complex networks. This methodology is a cyclic procedure involving four different processes: data mining, network analysis, computer simulation and collaboration. The data mining process is responsible for discovering potential associations and patterns. The network analysis process is responsible for assessing the discovery found in the data mining process and analyzing the network measures in the network. The computer simulation process is responsible for generating a validated model to simulate the evolution of the complex network based on the discoveries and measures used in the previous processes. Finally, the collaboration process is responsible for designing and maintaining a research collaboratory to host our research and support any possible similar research. To demonstrate the methodology, we applied this methodology in the study of Open Source Software movement, in particular, a study of the SourceForge.net development community. Through applying the methodology, we generated a distribution-based predictor to predict the ‘popularity’ of a project, had more insights about the structure and evolution of the SourceForge.net community network, generated a validated model to simulate the evolution of the community, and finally implemented and maintained a research collaboratory for the Open Source Software related research.