document: Recommendations to boost content spread in social networks Content sharing in social networks is a powerful mechanism for discovering content on the Internet . The degree to which content is disseminated within the network depends on the connectivity relationships among network nodes . Existing schemes for recommending connections in social networks are based on the number of common neighbors , similarity of user profiles , etc. . However , such similarity-based connections do not consider the amount of content discovered . In this paper , we propose novel algorithms for recommending connections that boost content propagation in a social network without compromising on the relevance of the recommendations . Unlike existing work on influence propagation , in our environment , we are looking for edges instead of nodes , with a bound on the number of incident edges per node . We show that the content spread function is not submodular , and develop approximation algorithms for computing a near-optimal set of edges . Through experiments on real-world social graphs such as Flickr and Twitter , we show that our approximation algorithms achieve content spreads that are as much as 90 times higher compared to existing heuristics for recommending connections .
doc_bio_tags: O O O B I O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O