An Efficient Method for Link Prediction in Complex Multiplex Networks

A great variety of artificial and natural systems can be abstracted into a set of entities interacting with each other. Such abstractions can very well represent the underlying dynamics of the system when modeled as network of vertices coupled by edges. Prediction of dynamics in these structures based on topological attribute or dependency relations is an important task. Link Prediction in such complex networks is regarded useful in almost all type of networks as it can be used to extract missing information, identify spurious interactions and evaluate network evolving mechanisms. Various similarity and likelihood based indices have been employed to infer different topological and relation based information to form a link prediction algorithm. These algorithms however are too specific in domain and/or do not encapsulate the generic nature of the real world information. In most natural and engineered systems, the entities are linked with multiple type of associations and relations which play a factor in the dynamics of the network. This forms a multiple subsystem or a multiple layer of networked information. These networks are regarded as Multiplex Networks. This work presents an approach for link prediction on Multiplex Networks where the associations are learned from the multiple layer of networks for link prediction purposes.