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Discovering Relational Implications in Multilayer Networks Using Formal Concept Analysis

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Information Integration and Web Intelligence (iiWAS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13635))

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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. 1.

    Notice that even if we conduct the implication discovery using this pruned set of edges, we still obtain the same set of implications.

References

  1. Alam, M., Buzmakov, A., Codocedo, V., Napoli, A.: Mining definitions from RDF annotations using formal concept analysis. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence, IJCAI (2015)

    Google Scholar 

  2. Aufaure, M.-A., Le Grand, B.: Advances in FCA-based applications for social networks analysis. Int. J. Concept. Struct. Smart Appl. 1(1), 73–89 (2013)

    Google Scholar 

  3. Bazhanov, K., Obiedkov, S.A.: Comparing performance of algorithms for generating the duquenne-guigues basis. In: International Conference on Concept Lattices and Their Applications, Nancy, France, pp. 43–57 (2011)

    Google Scholar 

  4. Bianconi, G.: Multilayer Networks: Structure and Function. Oxford University Press, Oxford (2018)

    Google Scholar 

  5. Cardillo, A., et al.: Emergence of Network Features from Multiplexity. Sci. Rep. 3(1), 1–6 (2013)

    Article  Google Scholar 

  6. Cuvelier, E., Aufaure, M.-A.: A buzz and e-reputation monitoring tool for twitter based on galois lattices. In: Andrews, S., Polovina, S., Hill, R., Akhgar, B. (eds.) ICCS 2011. LNCS (LNAI), vol. 6828, pp. 91–103. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22688-5_7

    Chapter  Google Scholar 

  7. De Domenico, M., Lancichinetti, A., Arenas, A., Rosvall, M.: Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems. Phys. Rev. X 5(1), 011027 (2015)

    Google Scholar 

  8. De Domenico, M., Nicosia, V., Arenas, A., Latora, V.: Structural Reducibility of multilayer networks. Nat. Commun. 6(6864), 1–9 (2015)

    Google Scholar 

  9. Ganter, B.: Two basic algorithms in concept analysis. In: Proceedings of Formal Concept Analysis, 8th International Conference, ICFCA 2010, Agadir, Morocco, 15–18 March 2010, pp. 312–340 (2010)

    Google Scholar 

  10. Ganter, B., Grigoriev, P.A., Kuznetsov, S.O., Samokhin, M.V.: Concept-based data mining with scaled labeled graphs. In: Wolff, K.E., Pfeiffer, H.D., Delugach, H.S. (eds.) ICCS-ConceptStruct 2004. LNCS (LNAI), vol. 3127, pp. 94–108. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-27769-9_6

    Chapter  Google Scholar 

  11. Ganter, B., Wille, R.: Formal Concept Analysis. Springer, Heidelberg (1999). https://doi.org/10.1007/978-3-642-59830-2

    Book  MATH  Google Scholar 

  12. Ghawi, R., Pfeffer, J.: Characterizing movie genres using formal concept analysis. In: Alam, M., Braun, T., Yun, B. (eds.) ICCS 2020. LNCS (LNAI), vol. 12277, pp. 132–141. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57855-8_10

    Chapter  Google Scholar 

  13. Ghawi, R., Pfeffer, J.: A community matching based approach to measuring layer similarity in multilayer networks. Soc. Netw. 68, 1–14 (2022)

    Article  Google Scholar 

  14. Guigues, J.-L., Duquenne, V.: Familles minimales d’implications informatives résultant d’un tableau de données binaires. Mathématiques et Sciences humaines 95, 5–18 (1986)

    Google Scholar 

  15. Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. In: Braslavski, P., Karpov, N., Worring, M., Volkovich, Y., Ignatov, D.I. (eds.) RuSSIR 2014. CCIS, vol. 505, pp. 42–141. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25485-2_3

    Chapter  Google Scholar 

  16. Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J.P., Moreno, Y., Porter, M.A.: Multilayer networks. J. Complex Netw. 2(3), 203–271 (2014)

    Article  Google Scholar 

  17. Kuznetsov, S.O.: Machine learning and formal concept analysis. In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 287–312. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24651-0_25

    Chapter  MATH  Google Scholar 

  18. Kuznetsov, S.O.: Fitting pattern structures to knowledge discovery in big data. In: Cellier, P., Distel, F., Ganter, B. (eds.) ICFCA 2013. LNCS (LNAI), vol. 7880, pp. 254–266. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38317-5_17

    Chapter  Google Scholar 

  19. Magnani, M., Micenková, B., Rossi, L.: Combinatorial analysis of multiple networks. CoRR, abs/1303.4986 (2013)

    Google Scholar 

  20. Newman, M.: Networks: An Introduction. Oxford University Press Inc, Oxford (2010)

    Google Scholar 

  21. Priss, U.: Formal concept analysis in information science. Annual Rev. Info. Sci. Technol. 40(1), 521–543 (2006)

    Article  Google Scholar 

  22. Schoenfeld, M., Pfeffer, J.: Networks and context: algorithmic challenges for context-aware social network research. In: Ragozini, G., Vitale, M.P. (eds.) Challenges in Social Network Research. LNSN, pp. 115–130. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-31463-7_8

    Chapter  Google Scholar 

  23. Stark, C., Breitkreutz, B.J., Reguly, T., Boucher, L., Breitkreutz, A., Tyers, M.: BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34(suppl_1), D535–D539 (2006)

    Google Scholar 

  24. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)

    Book  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Social Sciences and Technology, Technical University of Municgh, Munich, Germany

    Raji Ghawi & Jürgen Pfeffer

Authors
  1. Raji Ghawi
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  2. Jürgen Pfeffer
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Corresponding author

Correspondence to Raji Ghawi .

Editor information

Editors and Affiliations

  1. La Trobe University, Melbourne, VIC, Australia

    Eric Pardede

  2. Monash University, Melbourne, VIC, Australia

    Pari Delir Haghighi

  3. Johannes Kepler University Linz, Linz, Austria

    Ismail Khalil

  4. Johannes Kepler University Linz, Linz, Austria

    Gabriele Kotsis

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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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  • DOI: https://doi.org/10.1007/978-3-031-21047-1_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21046-4

  • Online ISBN: 978-3-031-21047-1

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