Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Optimal approximation for the submodular welfare problem in the value oracle model
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Do you know?: recommending people to invite into your social network
Proceedings of the 14th international conference on Intelligent user interfaces
Make new friends, but keep the old: recommending people on social networking sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Folks in Folksonomies: social link prediction from shared metadata
Proceedings of the third ACM international conference on Web search and data mining
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Suggesting friends using the implicit social graph
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Minimizing the diameter of a network using shortcut edges
SWAT'10 Proceedings of the 12th Scandinavian conference on Algorithm Theory
Gelling, and melting, large graphs by edge manipulation
Proceedings of the 21st ACM international conference on Information and knowledge management
Maximizing acceptance probability for active friending in online social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Link recommendation for promoting information diffusion in social networks
Proceedings of the 22nd international conference on World Wide Web companion
On popularity prediction of videos shared in online social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Proceedings of the 19th international conference on Intelligent User Interfaces
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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.