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Link prediction analysis becomes vital to acquire a deeper understanding ofevents underlying social networks interactions and connections especially incurrent evolving and large-scale social networks. Traditional link predictionapproaches underperformed for most large-scale social networks in terms of itsscalability and efficiency. Spark is a distributed open-source framework thatfacilitate scalable link prediction efficiency in large-scale social networks.The framework provides numerous tunable properties for users to manuallyconfigure the parameters for the applications. However, manual configurationsopen to performance issue when the applications start scaling tremendously,which is hard to set up and expose to human errors. This paper introduced anovel Self-Configured Framework (SCF) to provide an autonomous feature in Sparkthat predicts and sets the best configuration instantly before the applicationexecution using XGBoost classifier. SCF is evaluated on the Twitter socialnetwork using three link prediction applications: Graph Clustering (GC),Overlapping Community Detection (OCD), and Redundant Graph Clustering (RGD) toassess the impact of shifting data sizes on different applications in Twitter.The result demonstrates a 40% reduction in prediction time as well as abalanced resource consumption that makes full use of resources, especially forlimited number and size of clusters

Scalable link prediction in Twitter using self-configured framework