  Multiplex networks allow us to study a variety of complex systems where nodes
connect to each other in multiple ways, for example friend, family, and
co-worker relations in social networks. Link prediction is the branch of
network analysis allowing us to forecast the future status of a network: which
new connections are the most likely to appear in the future? In multiplex link
prediction we also ask: of which type? Because this last question is
unanswerable with classical link prediction, here we investigate the use of
graph association rules to inform multiplex link prediction. We derive such
rules by identifying all frequent patterns in a network via multiplex graph
mining, and then score each unobserved link's likelihood by finding the
occurrences of each rule in the original network. Association rules add new
abilities to multiplex link prediction: to predict new node arrivals, to
consider higher order structures with four or more nodes, and to be memory
efficient. In our experiments, we show that, exploiting graph association
rules, we are able to achieve a prediction performance close to an ideal
ensemble classifier. Further, we perform a case study on a signed multiplex
network, showing how graph association rules can provide valuable insights to
extend social balance theory.
