A Framework for Determining Effective Team Members Using Evolutionary Computation in Dynamic Social Networks

The team formation problem (TFP) concerns the process of bringing the experts together from Social Networks (SN) as teams in a collaborative working environment for a productive outcome. It was proven to be NP-hard problem. Our findings on a static SN using Evolutionary Computations (EC) achieved a significant improvement than State-of-Art methods on different datasets such as DBLP and Palliative care network. Since complexity and dynamics are challenging properties of real-world SN, our current research focuses on these properties in discovering new individuals for the teams. The process of detecting suitable members for teams is typically a real-time application of link prediction. Although different methods have been proposed to enhance the performance of link prediction, these methods need significant improvement in accuracy. Moreover, we examine the changes in attributes over time between individuals of the SN, especially on the co-authorship network. We introduce a time-varying score function, to evaluate the active researcher, that uses the number of new collaborations and number of frequent collaborations with existing connections. Moreover, we incorporate the shortest distance between any two individuals and introduces a score function to evaluate the skill similarity between any two individuals to form an effective team. We introduce Link prediction as a multi-objective optimization problem for optimizing three objectives, score of active researchers, skill similarity and shortest distance. We solved this problem by applying the NSGA-II and MOCA frameworks.