The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
IEEE Transactions on Knowledge and Data Engineering
Folks in Folksonomies: social link prediction from shared metadata
Proceedings of the third ACM international conference on Web search and data mining
LINKREC: a unified framework for link recommendation with user attributes and graph structure
Proceedings of the 19th international conference on World wide web
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Structural Predictors of Tie Formation in Twitter: Transitivity and Mutuality
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
Structural link analysis and prediction in microblogs
Proceedings of the 20th ACM international conference on Information and knowledge management
The utility of social and topical factors in anticipating repliers in Twitter conversations
Proceedings of the 5th Annual ACM Web Science Conference
Acquaintance or partner?: predicting partnership in online and location-based social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
W-entropy method to measure the influence of the members from social networks
International Journal of Web Engineering and Technology
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Existing approaches for link prediction, in the domain of network science, exploit a network's topology to predict future connections by assessing existing edges and connections, and inducing links given the presence of mutual nodes. Despite the rise in popularity of Attention-Information Networks (i.e. microblogging platforms) and the production of content within such platforms, no existing work has attempted to exploit the semantics of published content when predicting network links. In this paper we present an approach that fills this gap by a) predicting follower edges within a directed social network by exploiting concept graphs and thereby significantly outperforming a random baseline and models that rely solely on network topology information, and b) assessing the different behaviour that users exhibit when making followee-addition decisions. This latter contribution exposes latent factors within social networks and the existence of a clear need for topical affinity between users for a follow link to be created.