Performance Benchmarking for Link Prediction Algorithms in Social Networks


From a given snapshot of a social network database, we can predict whether a person can be potentially connected to another person, by analyzing existing links. We take two datasets (Facebook dataset from Stanford Large Network Dataset Collection and bibliography dataset from DBLP) and import that into MySQL, and Neo4J (Graph based DB) to evaluate the metrics for different network topology.


For the past one decade, social network has gained a lot of popularity and more users are making their online presence to connect. Hence, it brings up new challenges for analyzing data generated from these users. One such analysis is the social connection between two users. A lot of work has been done in the past with regard to link analysis. From a given snapshot of a social network database, we can predict for a given person (or the entire network), the people who she can be potentially connected to, by analyzing her existing links. Although there are is a lot of effort put into developing new prediction techniques, there is no solid function for analyzing which database is suited for a particular link analysis method. Link prediction can be done either for the entire network, or for a small subset of the network graph centered on a particular user. We consider the latter in this project.

We take open datasets and import it into MySQL (for relational), and Neo4J (for Graph based) and evaluate several link metrics. Experimentally, we plan on classifying how performance varies with respect to metrics for different databases. We also plan to analyze on how link metrics vary according to the network topology/parameters. We try to improve the performance of the queries implemented in the referenced paper : “Implementing link-prediction for social networks in a database system” by Sarah Cohen et al. Our experiment is performed on eight different real social network datasets taken from SNAP and DBLP databases. Finally, the results verify that the changes brought about in the neo4J schema and query structure improve the performance.