  Community detection in Social Networks is associated with finding and
grouping the most similar nodes inherent in the network. These similar nodes
are identified by computing tie strength. Stronger ties indicates higher
proximity shared by connected node pairs. This work is motivated by
Granovetter's argument that suggests that strong ties lies within densely
connected nodes and the theory that community cores in real-world networks are
densely connected. In this paper, we have introduced a novel method called
\emph{Disjoint Community detection using Cascades (DCC)} which demonstrates the
effectiveness of a new local density based tie strength measure on detecting
communities. Here, tie strength is utilized to decide the paths followed for
propagating information. The idea is to crawl through the tuple information of
cascades towards the community core guided by increasing tie strength.
Considering the cascade generation step, a novel preferential membership method
has been developed to assign community labels to unassigned nodes. The efficacy
of $DCC$ has been analyzed based on quality and accuracy on several real-world
datasets and baseline community detection algorithms.
