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Comparing Algorithmic Principles for Fuzzy Graph Communities over Neo4j

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Advances in Combining Intelligent Methods

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 116 ))

Abstract

Fuzzy graphs occur frequently in diverse fields such as computational neuroscience, social network analysis, devops, and information retrieval. This chapter covers an important class of fuzzy graphs where vertices are fixed whereas edges are fuzzy and exist according to a given or estimated probability distribution. Empirical evidence strongly suggests that, similarly to their deterministic counterparts, large fuzzy graphs of this type consist of recursively nested communities. The latter are closely linked to efficient local information dissemination and processing. Two community discovery algorithms, namely Fuzzy Walktrap and Fuzzy Newman-Girvan, based on different algorithm design principles are proposed and the performance of their Java implementation over Neo4j is experimentally assessed in terms of both total execution time and average graph cut cost on synthetic and real fuzzy graphs.

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Notes

  1. 1.

    https://dato.com/products/create/open_source.html.

  2. 2.

    https://brightstardb.com.

  3. 3.

    http://www.neo4j.com.

  4. 4.

    http://www.sparsity-technologies.com.

  5. 5.

    http://www.ontotext.com.

References

  1. Agichtein, E., Castillo, C., Donato, D., Gionis, A., Mishne, D.: Finding high-quality content in social media. In: Web Search and Data Mining Conference, WSDM, pp. 183–194. ACM (2008)

    Google Scholar 

  2. Bi, Z., Faloutsos, C., Korn, F.: The “dgx” distribution for mining massive, skewed data. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’01, pp. 17–26. ACM, New York, NY, USA (2001)

    Google Scholar 

  3. Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of community hierarchies in large networks. J. Stat. Mech.: Theory Exp. P1000 (2008)

    Google Scholar 

  4. Carrington, P.J., Scott, J., Wasserman, S.: Models and Methods in Social Network Analysis. Cambridge University Press (2005)

    Google Scholar 

  5. De Domenico, M., Lima, A., Mougel, P., Musolesi, M.: The anatomy of a scientific rumor. Sci. Rep. 3 (2013)

    Google Scholar 

  6. Deitrick W, Valyou B, Jones W, Timian J, Hu W (2013) Enhancing sentiment analysis on twitter using community detection. Commun. Netw. 5(3):192–197

    Article  Google Scholar 

  7. Drakopoulos, G., Baroutiadi, A., Megalooikonomou, V.: Higher order graph centrality measures for Neo4j. In: Proceedings of the 6th International Conference of Information, Intelligence, Systems, and Applications, IISA 2015. IEEE (2015)

    Google Scholar 

  8. Drakopoulos, G., Kanavos, A., Makris, C., Megalooikonomou, V.: On converting community detection algorithms for fuzzy graphs in Neo4j. In: Proceedings of the 5th International Workshop on Combinations of Intelligent Methods and Applications, CIMA 2015. IEEE (2015)

    Google Scholar 

  9. Drakopoulos, G., Megalooikonomou, V.: On the weight sparsity of multilayer perceptrons. In: Proceedings of the 6th International Conference of Information, Intelligence, Systems, and Applications, IISA 2015. IEEE (2015)

    Google Scholar 

  10. Fortunato S (2010) Community detection in graphs. Phys. Rep. 486:75–174

    Article  MathSciNet  Google Scholar 

  11. Girvan M, Newman M (2002) Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(2):7821–7826

    Article  MathSciNet  MATH  Google Scholar 

  12. Jurczyk, P., Agichtein, E.: Discovering authorities in question answer communities by using link analysis. In: Conference of Information and Knowledge Management, CIKM, pp. 919–922 (2007)

    Google Scholar 

  13. Kafeza, E., Kanavos, A., Makris, C., Chiu, D.: Identifying personality-based communities in social networks. In: Legal and Social Aspects in Web Modeling (Keynote Speech) in Conjunction with the International Conference on Conceptual Modeling (ER), LSAWM (2013)

    Google Scholar 

  14. Kafeza, E., Kanavos, A., Makris, C., Vikatos, P.: Predicting information diffusion patterns in twitter. In: Artificial Intelligence Applications and Innovations, AIAI, pp. 79–89 (2014)

    Google Scholar 

  15. Kafeza, E., Kanavos, A., Makris, C., Vikatos, P.: T-PICE: Twitter personality based influential communities extraction system. In: IEEE International Congress on Big Data, pp. 212–219 (2014)

    Google Scholar 

  16. Kanavos, A., Perikos, I.: Towards detecting emotional communities in twitter. In: 9th IEEE International Conference on Research Challenges in Information Science, RCIS, pp. 524–525 (2015)

    Google Scholar 

  17. Kanavos, A., Perikos, I., Vikatos, P., Hatzilygeroudis, I., Makris, C., Tsakalidis, A.: Conversation emotional modeling in social networks. In: 26th IEEE International Conference on Tools with Artificial Intelligence, ICTAI, pp. 478–484 (2014)

    Google Scholar 

  18. Kanavos, A., Perikos, I., Vikatos, P., Hatzilygeroudis, I., Makris, C., Tsakalidis, A.: Modeling retweet diffusion using emotional content. In: Artificial Intelligence Applications and Innovations, AIAI, pp. 101–110 (2014)

    Google Scholar 

  19. Kernighan B, Lin S (1970) An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. 49(1):291–307

    Article  MATH  Google Scholar 

  20. Kontopoulos, S., Drakopoulos, G.: A space efficient scheme for graph representation. In: Proceedings of the 26th International Conference on Tools with Artificial Intelligence, ICTAI 2014, pp. 299–303. IEEE (2014)

    Google Scholar 

  21. Leskovec J, Chakrabarti D, Kleinberg J, Faloutsos C, Ghahramani Z (2010) Kronecker graphs: an approach to modeling networks. J. Mach. Learn. Res. 11:985–1042

    MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  23. Panzarino, O.: Learning Cypher. PACKT Publishing (2014)

    Google Scholar 

  24. Pal, A., Counts, S.: Identifying topical authorities in microblogs. In: Web Search and Data Mining, WSDM, pp. 45–54 (2011)

    Google Scholar 

  25. Pivert, O., Thion, V., Jaudoin, H., Smits, G.: On a fuzzy algebra for querying graph databases. In: Proceedings of the 26th International Conference on Tools with Artificial Intelligence, ICTAI 2014, pp. 748–755. IEEE (2014)

    Google Scholar 

  26. Pons, P., Latapy, M.: Computing communities in large networks using random walks. arXiv:physics/0512106 (2005)

  27. Robinson, I., Webber, J., Eifrem, E.: Graph Databases. O’Reilly (2013)

    Google Scholar 

  28. Scott, J.: Social Network Analysis: A Handbook. SAGE Publications Ltd. (2000)

    Google Scholar 

  29. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8):888–905

    Article  Google Scholar 

  30. Weng, J., Lim, E.-P., Lim, J., Jiang, Q.H.: Twitterrank: Finding topic-sensitive influential twitterers. In: Web Search and Data Mining, WSDM, pp. 261–270 (2010)

    Google Scholar 

  31. Zamparas, V., Kanavos, A., Makris, C.: Real time analytics for measuring user influence on twitter. In: 27th IEEE International Conference on Tools with Artificial Intelligence, ICTAI (2015)

    Google Scholar 

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Authors and Affiliations

  1. Computer Engineering and Informatics Department, University of Patras, Patras, 26500, Hellas

    Georgios Drakopoulos, Andreas Kanavos, Christos Makris & Vasileios Megalooikonomou

Authors
  1. Georgios Drakopoulos
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  2. Andreas Kanavos
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  3. Christos Makris
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  4. Vasileios Megalooikonomou
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Corresponding author

Correspondence to Georgios Drakopoulos .

Editor information

Editors and Affiliations

  1. Sch of Engg, Dept of Comp Engg & Informa, University of Patras Sch of Engg, Dept of Comp Engg & Informa, Patras, Greece

    Ioannis Hatzilygeroudis

  2. Fac of Engg & Compu, dept of Compu, Coventry University, Reader in Perv, Fac of Engg & Compu, dept of Compu, Coventry, United Kingdom

    Vasile Palade

  3. Dept of Edu Sci Early child, Lab of info, Democritus Univer Thrace,Sch of Edu Sci Dept of Edu Sci Early child, Lab of info, Alexandroupolis, Greece

    Jim Prentzas

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Drakopoulos, G., Kanavos, A., Makris, C., Megalooikonomou, V. (2017). Comparing Algorithmic Principles for Fuzzy Graph Communities over Neo4j. In: Hatzilygeroudis, I., Palade, V., Prentzas, J. (eds) Advances in Combining Intelligent Methods. Intelligent Systems Reference Library, vol 116 . Springer, Cham. https://doi.org/10.1007/978-3-319-46200-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-46200-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46199-1

  • Online ISBN: 978-3-319-46200-4

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