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