Social friend recommendation based on multiple network correlation
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The document discusses an innovative algorithm for social friend recommendation based on network correlation, introduced to improve upon traditional methods that often overlook important social network characteristics. This new approach aligns related social role networks and preserves their structure, allowing for better friend recommendations. Experiments conducted on the Flickr network demonstrate that this algorithm significantly outperforms existing methods in accuracy.
Social friend recommendation based on multiple network correlation
- 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.