Enabling Link Prediction Optimization on Social Networks

Virtual social networks are a modern age of spaces that play an important role in the world today. They are highly dynamic networks with a complex structure. This is why it is very difficult to predict communication in this field. The prediction has recently caught the attention of various researchers as one of the most important aspects of data mining. In addition to understanding the relationship between groups in social communities, the connection prediction in social networks also ensures that networks are popular. Link prediction is the prediction of the probability that two entities will interact based on some unique and common characteristics between them. Link prediction is intended to generate and propose a list of persons to whom the user communicates. This study introduces a prediction approach used to combine a genetic algorithm with an algorithm from Louvain. Data are first chosen from the default dataset as binary in this process. Then the best nodes are extracted and chosen based on merit using the genetic algorithm. Lastly, the modularity of the networks is obtained by using the Louvain algorithm. The findings indicated the optimal performance of this approach.