IMPLEMENTATION OF NETWORK CORRELATION METHOD TO ACHIEVE PRIVACY IN SOCIAL MEDIA COMMUNICATIONS

Friend recommendation is an important recommender application in social media. Major social websites such as Twitter and Facebook are all capable of recommending friends to individuals. However, most of these websites use simple friend recommendation algorithms such as similarity, popularity, or “friend's friends are friends”, which are intuitive but consider few of the characteristics of the social network. In this paper we investigate the structure of social networks and develop an algorithm for Network Correlation-based Social Friend Recommendation (NC-based SFR). To accomplish this goal, we correlate dif erent “social role” networks, find their relationships and make friend recommendations. NC-based SFR is characterized by two key components: 1) Related networks are aligned by selecting important features from each network. 2) The network structure should be maximally preserved before and after network alignment. After important feature selection has been made, we recommend friends based on these features. We conduct experiments on the Flickr network, which contains morethantenthousandnodesandover30thousandtagscovering half a million photos, to show that the proposed algorithm recommends friends more precisely than reference methods.