  Social networks have become an inseparable part of human life and processing
them in an efficient manner is a top priority in the study of networks. These
networks are highly dynamic and they are growing incessantly. Inspired by the
concept of triadic closure, we propose a probabilistic mechanism to model the
evolution of these dynamic graphs. Although triadic closure is ubiquitous in
social networks and its presence helps forming communities, probabilistic
models encapsulating it have not been studied adequately.
  We theoretically analyze our model and show how to bound the growth rate of
some characteristics of the graph, such as degree of vertices. Leveraging our
theoretical results, we develop a scheduling subroutine to process
modifications of the graph in batches. Our scheduling subroutine is then used
to speed up the state-of-the-art algorithms with negligible loss in their
approximation guarantees. We demonstrate the applicability of our method by
applying it to the densest subgraph and tri-densest subgraph discovery problem.
