CommuNety: A Deep Learning System for the Prediction of Cohesive Social Communities
This work addresses the challenge of mining social media for cohesive communities, which is important for social network analysis and recommendation systems, but it appears incremental as it builds on existing deep learning and image analysis techniques.
The paper tackles the problem of predicting cohesive social communities from social media by using images instead of text, proposing a deep learning system called CommuNety that includes a hierarchical CNN and novel algorithms for face co-occurrence and photo ranking, and demonstrates superior performance on the PIPA dataset compared to state-of-the-art methods.
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.