Abstract
Many real world networks are multi-relational exhibiting multiple types of relations between nodes. In such complex systems, some of the interaction layers can be dependent on other layers. Unveiling this kind of relational implications among the different layers of a multilayer network is crucial to understand its dynamic properties, and to reveal new non-trivial structural properties. We propose a method, based on Formal Concept Analysis, to discover the implication rules between the different layers in a multilayer network. We demonstrate the usefulness of this method using two large real-world multilayer networks. We also explore how such discovered implications can be exploited in a link prediction task, and the results show that this approach can achieve a good accuracy of 77% for one of the networks.
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Notes
- 1.
Notice that even if we conduct the implication discovery using this pruned set of edges, we still obtain the same set of implications.
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Ghawi, R., Pfeffer, J. (2022). Discovering Relational Implications in Multilayer Networks Using Formal Concept Analysis. In: Pardede, E., Delir Haghighi, P., Khalil, I., Kotsis, G. (eds) Information Integration and Web Intelligence. iiWAS 2022. Lecture Notes in Computer Science, vol 13635. Springer, Cham. https://doi.org/10.1007/978-3-031-21047-1_29
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