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FCA in a Logical Programming Setting for Visualization-Oriented Graph Compression

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Formal Concept Analysis (ICFCA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10308))

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Abstract

Molecular biology produces and accumulates huge amounts of data that are generally integrated within graphs of molecules linked by various interactions. Exploring potentially interesting substructures (clusters, motifs) within such graphs requires proper abstraction and visualization methods. Most layout techniques (edge and nodes spatial organization) prove insufficient in this case. Royer et al. introduced in 2008 Power graph analysis, a dedicated program using classes of nodes with similar properties and classes of edges linking node classes to achieve a lossless graph compression. The contributions of this paper are twofold. First, we formulate and study this issue in the framework of Formal Concept Analysis. This leads to a generalized view of the initial problem offering new variants and solving approaches. Second, we state the FCA modeling problem in a logical setting, Answer Set programming, which provides a great flexibility for the specification of concept search spaces.

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Notes

  1. 1.

    Repository at http://powergrasp.bourneuf.net.

  2. 2.

    powergrasp/tests/.

  3. 3.

    powergrasp/ASPSources/postprocessing.lp.

  4. 4.

    powergrasp/ASPSources/findbestorientedbiclique.lp.

  5. 5.

    powergrasp/ASPSources/by_priority.lp.

  6. 6.

    powergrasp/ASPSources/by_fuzzy_priority.lp.

References

  1. Agarwal, P.K., Alon, N., Aronov, B., Suri, S.: Can visibility graphs be represented compactly? Discret. Comput. Geom. 12(3), 347–365 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  2. Ahnert, S.E.: Generalised power graph compression reveals dominant relationship patterns in complex networks. Sci. Rep. 4, Article no. 4385 (2014). https://www.nature.com/articles/srep04385

  3. Alexe, G., Alexe, S., Crama, Y., Foldes, S., Hammer, P.L., Simeone, B.: Consensus algorithms for the generation of all maximal bicliques. Discret. Appl. Math. 145(1), 11–21 (2004). Graph Optimization IV

    Article  MathSciNet  MATH  Google Scholar 

  4. Amilhastre, J., Vilarem, M., Janssen, P.: Complexity of minimum biclique cover and minimum biclique decomposition for bipartite domino-free graphs. Discret. Appl. Math. 86(2–3), 125–144 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  5. Bu, D., Zhao, Y., Cai, L., Xue, H., Zhu, X., Lu, H., Zhang, J., Sun, S., Ling, L., Zhang, N., Li, G., Chen, R.: Topological structure analysis of the protein–protein interaction network in budding yeast. Nucleic Acids Res. 31(9), 2443–2450 (2003)

    Article  Google Scholar 

  6. Chein, M.: Algorithme de recherche des sous-matrices premiÈres d’une matrice. Bulletin mathématique de la Société des Sciences Mathématiques de la République Socialiste de Roumanie 13(61)(1), 21–25 (1969)

    Google Scholar 

  7. Chung, F.: On the coverings of graphs. Discret. Math. 30(2), 89–93 (1980)

    Article  MATH  Google Scholar 

  8. Daminelli, S., Haupt, V.J., Reimann, M., Schroeder, M.: Drug repositioning through incomplete bi-cliques in an integrated drug–target–disease network. Integr. Biol. 4(7), 778–788 (2012)

    Article  Google Scholar 

  9. Dwyer, T., Henry Riche, N., Marriott, K., Mears, C.: Edge compression techniques for visualization of dense directed graphs. IEEE Trans. Vis. Comput. Graph. 19(12), 2596–2605 (2013)

    Article  Google Scholar 

  10. Dwyer, T., Mears, C., Morgan, K., Niven, T., Marriott, K., Wallace, M.: Improved optimal and approximate power graph compression for clearer visualisation of dense graphs. CoRR, abs/1311.6996 (2013)

    Google Scholar 

  11. Eppstein, D.: Arboricity and bipartite subgraph listing algorithms. Inf. Process. Lett. 51(4), 207–211 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  12. Gagneur, J., Krause, R., Bouwmeester, T., Casari, G.: Modular decomposition of protein-protein interaction networks. Genome Biol. 5(8), R57 (2004)

    Article  Google Scholar 

  13. Gebser, M., Kaminski, R., Kaufmann, B., Lindauer, M., Ostrowski, M., Romero, J., Schaub, T., Thiele, S.: Potassco User Guide (2015)

    Google Scholar 

  14. Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., Schneider, M.: Potassco: the Potsdam answer set solving collection. AI Commun. 24(2), 107–124 (2011)

    MathSciNet  MATH  Google Scholar 

  15. Gelfond, M., Lifschitz, V.: Logic programs with classical negation. In: Proceedings of 7th International Conference on Logic Programming (ICLP), pp. 579–97 (1990)

    Google Scholar 

  16. Hitzler, P., Krötzsch, M.: Querying formal contexts with answer set programs. In: Schärfe, H., Hitzler, P., Øhrstrøm, P. (eds.) ICCS-ConceptStruct 2006. LNCS, vol. 4068, pp. 260–273. Springer, Heidelberg (2006). doi:10.1007/11787181_19

    Chapter  Google Scholar 

  17. Jukna, S., Kulikov, A.: On covering graphs by complete bipartite subgraphs. Discret. Math. 309(10), 3399–3403 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  18. King, A.D., Pržulj, N., Jurisica, I.: Protein complex prediction via cost-based clustering. Bioinformatics 20(17), 3013–3020 (2004)

    Article  Google Scholar 

  19. Launay, G., Salza, R., Multedo, D., Thierry-Mieg, N., Ricard-Blum, S.: Matrixdb, the extracellular matrix interaction database: updated content, a new navigator and expanded functionalities. Nucleic Acids Res. 43(D1), D321–D327 (2015)

    Article  Google Scholar 

  20. Lifschitz, V.: What is answer set programming? In: Proceedings of 23rd National Conference on Artificial Intelligence, AAAI 2008, vol. 3, pp. 1594–1597. AAAI Press (2008)

    Google Scholar 

  21. Navlakha, S., Schatz, M.C., Kingsford, C.: Revealing biological modules via graph summarization. J. Comput. Biol. 16(2), 253–264 (2009)

    Article  Google Scholar 

  22. Ogata, H., Fujibuchi, W., Goto, S., Kanehisa, M.: A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters. Nucleic Acids Res. 28(20), 4021–4028 (2000)

    Article  Google Scholar 

  23. Royer, L., Reimann, M., Andreopoulos, B., Schroeder, M.: Unraveling protein networks with power graph analysis. PLoS Comput. Biol. 4(7), e1000108 (2008)

    Article  MathSciNet  Google Scholar 

  24. Rudolph, S., Săcărea, C., Troancă, D.: Membership constraints in formal concept analysis. In: Proceedings of 24th International Conference on Artificial Intelligence, IJCAI 2015, pp. 3186–3192. AAAI Press (2015)

    Google Scholar 

  25. Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D.E.A.: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13(11), 2498–2504 (2003)

    Article  Google Scholar 

  26. Tsatsaronis, G., Reimann, M., Varlamis, I., Gkorgkas, O., Nørvåg, K.: Efficient community detection using power graph analysis. In: Proceedings of 9th Workshop on Large-Scale and Distributed Informational Retrieval, pp. 21–26. ACM (2011)

    Google Scholar 

  27. Wucher, V.: Modeling of a gene network between mRNAs and miRNAs to predict gene functions involved in phenotypic plasticity in the pea aphid. Thesis, Université Rennes 1, November 2014

    Google Scholar 

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Acknowledgments

We wish to thank D. Tagu (INRA Le Rheu) and N. Théret (Inserm) for providing us the networks used in the results section. We would also like to express our gratitude to the reviewers for their feedbacks.

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Correspondence to Lucas Bourneuf or Jacques Nicolas .

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Bourneuf, L., Nicolas, J. (2017). FCA in a Logical Programming Setting for Visualization-Oriented Graph Compression. In: Bertet, K., Borchmann, D., Cellier, P., Ferré, S. (eds) Formal Concept Analysis. ICFCA 2017. Lecture Notes in Computer Science(), vol 10308. Springer, Cham. https://doi.org/10.1007/978-3-319-59271-8_6

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  • DOI: https://doi.org/10.1007/978-3-319-59271-8_6

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