The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
SoQL: A Language for Querying and Creating Data in Social Networks
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Databases and Social Networks
Discovering top-k teams of experts with/without a leader in social networks
Proceedings of the 20th ACM international conference on Information and knowledge management
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Storing and querying large social networks is a challenging problem, due both to the scale of the data, and to intricate querying requirements. One common type of query over a social network is link prediction, which is used to suggest new friends for existing nodes in the network. There is no gold standard metric for predicting new links. However, past work has been effective at identifying a number of metrics that work well for this problem. These metrics vastly differ one from another in their computational complexity, e.g., they may consider a small neighborhood of a node for which new links should be predicted, or they may perform random walks over the entire social network graph. This paper considers the problem of implementing metrics for link prediction in a social network over different types of database systems. We consider the use of a relational database, a key-value store and a graph database. We show the type of database system affects the ease in which link prediction may be performed. Our results are empirically validated by extensive experimentation over real social networks of varying sizes.