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Interests Propagation in Computer Science Research Community

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Proceedings of ECCS 2014

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

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Abstract

This work proposes a framework to study the propagation of individual interests in scientific social networks. We analyze the domain of computer science and we profile members of the social network by means of semantic techniques. We model the evolution of interests as a diffusion process and we measure individual features, such as members’ susceptibilities and authorities. The DBLP (Digital Bibliography and Library Project) dataset has been selected as main source since it provides an extensive list of scientific publications in this field.

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References

  1. Aral, S., Muchnik, L., Sundararajan, A.: Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc. Nat. Acad. Sci. 106(51), 21544–21549 (2009)

    Google Scholar 

  2. Aral, S., Walker, D.: Identifying influential and susceptible members of social networks. Science 337(6092), 337–341 (2012)

    Article  ADS  MathSciNet  Google Scholar 

  3. Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  4. Bojārs, U., Breslin, J., Finn, A., Decker, S.: Using the semantic web for linking and reusing data across web 2.0 communities. Web Seman.: Sci., Ser. Agents World Wide Web 6(1), 21–28 (2008)

    Article  Google Scholar 

  5. Castellano, C., Fortunato, S., Loreto, V.: Statistical physics of social dynamics. Rev. Mod. Phys. 81(2), 591 (2009)

    Article  ADS  Google Scholar 

  6. Cucchiarelli, A., D’Antonio, F., Velardi, P.: Semantically interconnected social networks. Soc. Netw. Anal. Min. 2(1), 69–95 (2012)

    Article  Google Scholar 

  7. D’Agostino, G., D’Antonio, F., De Nicola, A., Tucci, S.: Interests diffusion in social networks. Physica A 436, 443–461 (2015)

    Article  ADS  MathSciNet  Google Scholar 

  8. De Nicola, A., Missikoff, M., Navigli, R.: A software engineering approach to ontology building. Inf. Syst. 34(2), 258–275 (2009)

    Article  Google Scholar 

  9. Galam, S.: Local dynamics vs. social mechanisms: A unifying frame. EPL (Europhys. Lett.) 70(6), 705 (2005)

    Article  ADS  Google Scholar 

  10. Gomez, S., Diaz-Guilera, A., Gomez-Gardeñes, J., Perez-Vicente, C.J., Moreno, Y., Arenas, A.: Diffusion dynamics on multiplex networks. Phys. Rev. Lett. 110(2), 028701 (2013)

    Article  ADS  Google Scholar 

  11. Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, pp. 241–250. WSDM ’10, ACM (2010)

    Google Scholar 

  12. Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. KDD ’03, ACM (2003)

    Google Scholar 

  13. Manski, C.F.: Identification Problems in the Social Sciences. Harvard University Press (1995)

    Google Scholar 

  14. Mika, P.: Ontologies are us: A unified model of social networks and semantics. Web Seman.: Sci., Serv. Agents World Wide Web 5(1), 5–15 (2007)

    Article  MathSciNet  Google Scholar 

  15. Navigli, R., Velardi, P.: Learning domain ontologies from document warehouses and dedicated web sites. Comput. Linguist. 30(2), 151–179 (2004)

    Article  MATH  Google Scholar 

  16. Palla, G., Vicsek, T.: Statistical properties of community dynamics in large social networks. Int. J. Agent Technol. Syst. (IJATS) 1(4), 1–16 (2009)

    Article  Google Scholar 

  17. Pastor-Satorras, R., Vespignani, A.: Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86, 3200–3203 (2001)

    Article  ADS  Google Scholar 

  18. Petersen, A.M., Fortunato, S., Pan, R.K., Kaski, K., Penner, O., Rungi, A., Riccaboni, M., Stanley, H.E., Pammolli, F.: Reputation and impact in academic careers. Proc. Nat. Acad. Sci. 111(43), 15316–15321 (2014)

    Google Scholar 

  19. Quattrociocchi, W., Caldarelli, G., Scala, A.: Opinion dynamics on interacting networks: media competition and social influence. Sci. R. 4 (2014)

    Google Scholar 

  20. Resnik, P.: Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. J. Artif. Intell. Res. 11, 95–130 (1998)

    MATH  Google Scholar 

  21. Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61–70. KDD ’02, ACM (2002)

    Google Scholar 

  22. Shao, J., Havlin, S., Stanley, H.E.: Dynamic opinion model and invasion percolation. Phys. Rev. Lett. 103(1), 018701 (2009)

    Article  ADS  Google Scholar 

  23. Sowa, J.F.: Semantic networks. Encyclopedia of Cognitive Science (2006)

    Google Scholar 

  24. Vespignani, A.: Modelling dynamical processes in complex socio-technical systems. Nat. Phys. 8(1), 32–39 (2012)

    Article  MathSciNet  Google Scholar 

  25. Wang, D., Song, C., Barabási, A.L.: Quantifying long-term scientific impact. Science 342(6154), 127–132 (2013)

    Article  ADS  Google Scholar 

  26. Wang, D., Wen, Z., Tong, H., Lin, C.Y., Song, C., Barabási, A.L.: Information spreading in context. In: Proceedings of the 20th International Conference on World Wide Web. pp. 735–744. WWW ’11, ACM, New York, NY, USA (2011)

    Google Scholar 

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Acknowledgments

The authors are indebted to Fulvio D’Antonio for providing the preliminary set of topics. Salvatore Tucci, Emiliano Casalicchio, and Francesco Lo Presti are kindly acknowledged for stimulating discussions.

Author information

Authors and Affiliations

  1. ENEA-CR Casaccia, Rome, Italy

    Gregorio D’Agostino & Antonio De Nicola

  2. University of Rome Tor Vergata, Rome, Italy

    Antonio De Nicola

  3. Center for Polymer Studies, Boston University, Boston, MA, USA

    Gregorio D’Agostino

Authors
  1. Gregorio D’Agostino
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  2. Antonio De Nicola
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Corresponding author

Correspondence to Antonio De Nicola .

Editor information

Editors and Affiliations

  1. RAF, University of Zürich, Zürich, Switzerland

    Stefano Battiston

  2. CREATE-NET, Trento, Italy

    Francesco De Pellegrini

  3. IMT Lucca, Lucca, Italy

    Guido Caldarelli

  4. School of Science and Technology, University of Camerino, Camerino, Italy

    Emanuela Merelli

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D’Agostino, G., De Nicola, A. (2016). Interests Propagation in Computer Science Research Community. In: Battiston, S., De Pellegrini, F., Caldarelli, G., Merelli, E. (eds) Proceedings of ECCS 2014. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-29228-1_12

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