Community Detection in Anonymized Social Networks

Social media and social networks are embedded in our society to a point that could not have been imagined only ten years ago. Facebook, LinkedIn, and Twitter are already well known social networks that have a large audience in all age groups. Recently more trendy social sites such as Pinterest, Instagram, Vine, Tumblr, WhatsApp, and Snapchat are being preferred by the younger audience. The amount of data that those social sites gather from their users is continually increasing and this data is very valuable for marketing, research, and various other purposes. At the same time, this data usually contain a significant amount of sensitive information which should be protected against unauthorized disclosure. To protect the privacy of individuals, this data must be anonymized such that the risk of re-identification of specific individuals is very low. In this paper we study how well anonymized social networks preserve existing communities from the original social networks. To anonymize social networks we used two models, namely, k-anonymity for social networks and kdegree anonymity. To determine communities in social networks we used a community detection algorithm based on modularity quality function known as Louvain method. Our experiments on publically available datasets show that anonymized social networks satisfactorily preserve the community structure of their original networks.