TY - GEN
T1 - Discovering Relational Implications in Multilayer Networks Using Formal Concept Analysis
AU - Ghawi, Raji
AU - Pfeffer, Jürgen
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Formal concept analysis
KW - Implications
KW - Link prediction
KW - Multilayer networks
UR - https://www.scopus.com/pages/publications/85145007014
U2 - 10.1007/978-3-031-21047-1_29
DO - 10.1007/978-3-031-21047-1_29
M3 - Conference contribution
AN - SCOPUS:85145007014
SN - 9783031210464
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 352
EP - 366
BT - Information Integration and Web Intelligence - 24th International Conference, iiWAS 2022, Proceedings
A2 - Pardede, Eric
A2 - Delir Haghighi, Pari
A2 - Khalil, Ismail
A2 - Kotsis, Gabriele
PB - Springer Science and Business Media Deutschland GmbH
T2 - 24th International Conference on Information Integration and Web Intelligence, iiWAS 2022, held in conjunction with the 20th International Conference on Advances in Mobile Computing and Multimedia Intelligence, MoMM 2022
Y2 - 28 November 2022 through 30 November 2022
ER -