  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. We improve over previous work by creating a framework that is also
efficient in terms of runtime, which enables an increase in prediction
performance. This increase in efficiency allows us to improve a case study on a
signed multiplex network, showing how graph association rules can provide
valuable insights to extend social balance theory.
