  Effective mining of social media, which consists of a large number of users
is a challenging task. Traditional approaches rely on the analysis of text data
related to users to accomplish this task. However, text data lacks significant
information about the social users and their associated groups. In this paper,
we propose CommuNety, a deep learning system for the prediction of cohesive
social networks using images. The proposed deep learning model consists of
hierarchical CNN architecture to learn descriptive features related to each
cohesive network. The paper also proposes a novel Face Co-occurrence Frequency
algorithm to quantify existence of people in images, and a novel photo ranking
method to analyze the strength of relationship between different individuals in
a predicted social network. We extensively evaluate the proposed technique on
PIPA dataset and compare with state-of-the-art methods. Our experimental
results demonstrate the superior performance of the proposed technique for the
prediction of relationship between different individuals and the cohesiveness
of communities.
