Friend recommendation with content spread enhancement in social networks

Social network is becoming an increasingly popular media for information sharing. More and more people are interacting with others via major social network sites such as Twitter and Flickr. An important aspect of a social network is its capability in efficiently spreading content, not only within a small circle but also in the whole network. However, most existing methods for recommending friends in social networks only aim at achieving high recommendation success rate. The network grown from such recommendations is not optimized for content spread. In this paper, we propose a novel friend recommendation method ACR-FoF (algebraic connectivity regularized friends-of-friends) that considers both success rate and content spread in the network. Using the algebraic connectivity of a connected network to estimate its capability for spreading contents, our recommendation method naturally extends existing friend recommendation algorithms such as FoF to achieve both recommendation relevance and content spread in a social network. Experimental results on simulated and real social network data sets show that our method can significantly improve content spread in a social network with only a very tiny compromise on friend recommendation success rate.