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Effective mining of social media, which consists of a large number of usersis a challenging task. Traditional approaches rely on the analysis of text datarelated to users to accomplish this task. However, text data lacks significantinformation about the social users and their associated groups. In this paper,we propose CommuNety, a deep learning system for the prediction of cohesivesocial networks using images. The proposed deep learning model consists ofhierarchical CNN architecture to learn descriptive features related to eachcohesive network. The paper also proposes a novel Face Co-occurrence Frequencyalgorithm to quantify existence of people in images, and a novel photo rankingmethod to analyze the strength of relationship between different individuals ina predicted social network. We extensively evaluate the proposed technique onPIPA dataset and compare with state-of-the-art methods. Our experimentalresults demonstrate the superior performance of the proposed technique for theprediction of relationship between different individuals and the cohesivenessof communities.

CommuNety: A Deep Learning System for the Prediction of Cohesive Social Communities