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SOCIAL FRIEND RECOMMENDATION BASED ON MULTIPLE NETWORK
CORRELATION
ABSTRACT:
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 different
“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, and 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 more than ten thousand nodes and over 30 thousand tags covering
half a million photos, to show that the proposed algorithm recommends friends more
precisely than reference methods.

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  • 1. #13/ 19, 1st Floor, Municipal Colony, Kangayanellore Road, Gandhi Nagar, Vellore – 6. Off: 0416-2247353 / 6066663 Mo: +91 9500218218 Website: www.shakastech.com, www.shakastech.weebly.com, Email - id: shakastech@gmail.com, info@shakastech.com SOCIAL FRIEND RECOMMENDATION BASED ON MULTIPLE NETWORK CORRELATION ABSTRACT: 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 different “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, and 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 more than ten thousand nodes and over 30 thousand tags covering half a million photos, to show that the proposed algorithm recommends friends more precisely than reference methods.