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Semantic social networks analysis

Towards a sociophysical knowledge analysis

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Abstract

In 1977, Freeman formalised generic measures of social networks analysis (SNA). Then, the Web 2.0 social networks have become global networks (e.g., FaceBook, MSN). This article presents a semantic model, non probabilistic and predictive, for the decisional analysis of professional and institutional social networks. The presented multidisciplinary model, in parallel to Galam sociophysics, integrates some semantic methods of knowledge engineering and natural language processing, some measures of statistic sociology and some electrodynamic laws, applied to the economic performance and social climate optimisation. It is currently under experimentation, in line with the Socioprise project, funded by the French State Secretariat at the prospective and development of the digital economy.

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Notes

  1. The degree of a vertex is the number of edges connected to it. Connectivity depends on the number of vertices accessible from a vertex, and the distance between two vertices is given by the number of edges of the shortest chain connecting them.

  2. The social risk means the risk factor present in the social structure, which is able to cause disease or trauma.

  3. We talk of favours network when the social graph structure depends on peer-to-peer evaluations.

  4. Entropy generally expresses the disorder degree of a system.

  5. Some index entries have no association in the thesaurus.

  6. The other arguments passed to \(Simprox\) enables to adapt the heuristic of the measure to various uses, such as the separate analysis of incoming or outgoing flows in oriented graphs.

  7. The clusters are built so as to minimise interconnections between the nodes of distinct clusters.

  8. In order to respect the theoretical framework in which the mesh law is verified, we consider that the elements of \(aKk\) and of \(Kk\) are some non-radiant dipoles, i.e., neutral from the electrostatic and electromagnetic standpoints. In order to respect the coherence of the real model with the electrical metaphor, we consider that the nodes of \(Kk\) are connected to the ground, i.e., null potential difference or tension.

  9. Due to the project industrial maturity and intellectual property rights, we do not provide more experience feedback or simulation results.

References

  • Andrade N, Brasileiro F, Cirne W, Mowbray M (2007) Automatic grid assembly by promoting collaboration in peer-to-peer grids. J Parallel Distrib Comput 67(8):957–966. doi:10.1016/j.jpdc.2007.04.011

    Google Scholar 

  • Beauchamp Murray A (1965) An improved index of centrality. Behav Sci 10(2):161–163. doi:10.1002/bs.3830100205

    Google Scholar 

  • Bhattacharyya P, Garg A, Wu S (2011) Analysis of user keyword similarity in online social networks. Soc Netw Anal Min 3:143–158

    Article  Google Scholar 

  • Brandes U, Fleischer D (2005) Centrality measures based on current flow. In: 22nd Symp. theoretical aspects of computer science (STACS 05). LNCS, vol 3404. Springer, Berlin, pp 533–544

  • Brehm J (1966) A theory of psychological reactance. Academic Press, New York

  • Clauset A, Newman M, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70:6. doi:10.1103/PhysRevE.70.066111, http://www.ece.unm.edu/ifis/papers/community-moore.pdf

  • Eéréto G (2011) Analyse sémantique des réseaux sociaux. Ph.D. thesis, Université de Nice Sophia-Antipolis, Laboratoire d’Informatique, Signaux, et Systémes de Sophia-Antipolis (L3S, UMR6070 CNRS)

  • Freeman L, Bloomberg W, Koff S, Sunshine M, Fararo T (1960) Local community leadership. Syracuse University College, Syracuse

  • Freeman L (1979) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239. 10.1016/0378-8733(78)90021-7

    Google Scholar 

  • Freeman L, White D, Romney A (1989) Research methods in social network analysis. George Mason University Press

  • Freeman L (1977) A set of measures of centrality based on betweenness. Sociometry 40:35–41

    Article  Google Scholar 

  • Galam S (2008) Sociophysics: a review of Galam models. Int J Mod Phys C 19(3):409

    Article  MATH  Google Scholar 

  • Gruber T (1995) Toward principles for the design of ontologies used for knowledge sharing. Int J Human Comput Stud 43(5/6):907–928

    Article  Google Scholar 

  • Gruber RT (2008) Collective knowledge systems: where the social web meets the semantic web. Web Semant Sci Serv Agents World Wide Web. 6(1):4–13

    Article  Google Scholar 

  • Jaccard P (1901) Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines. Bulletin de la Société Vaudoise des Sciences Naturelles 37:241–272

    Google Scholar 

  • Macskassy S (2011) Contextual linking behavior of bloggers: leveraging text mining to enable topic-based analysis. Soc Netw Anal Min 4:355–375

    Article  Google Scholar 

  • Mika P (2005) Ontologies are us: a unified model of social networks and semantics. In: International semantic web conference. Lecture notes in computer science, vol 3729. International Semantic Web Conference 2005. Springer, Berlin, pp 522–536. doi:10.1.1.60.2861

  • Moreno J (1934) Who shall survive?—(Trad. fr) Fondements de la sociométrie, PUF

  • Newman M (2004) Detecting community structure in networks. Eur Phys J B Condens Matter Complex Syst 38(2):321–320. doi:10.1140/epjb/e2004-00124-y

    Google Scholar 

  • Newman M (2006) Finding community structure in networks using the eigenvectors of matrices. Phys Rev E 74(3):19. doi:10.1103/PhysRevE.74.036104

    Google Scholar 

  • Newman M (2005) Finding community structure in networks using the eigenvectors of matrices. Soc Netw 27(1):39. doi:10.1016/j.socnet.2004.11.009

    Google Scholar 

  • Pearson M, West P (2003) Drifting smoke rings: social network analysis and Markov processes in a longitudinal study of friendship groups and risk taking. Connect Bull Int Netw Soc Netw Anal 25(2):59–76. http://eprints.gla.ac.uk/2701/

  • Peters C, Nunzio G.D, Kurimo M, Mandl T, Mostefa D, Peñas A, Roda G (eds) (2010) Unsupervised morphological analysis by formal analogy. Lecture notes in computer science. Springer, Berlin

  • Pitrat J (1990) Métaconnaissance: Futur de l’intelligence artificielle (Hermès)

  • Robertson SE, Sparck Jones K (1976) Relevance weighting of search terms. J Am Soc Inf Sci 27(3):129–146

    Article  Google Scholar 

  • Shannon C (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–453

    MathSciNet  MATH  Google Scholar 

  • Then H, Feng M, Holonyak N (2010) Microwave circuit model of the three-port transistor laser. J Appl Phys 107(9):094509. doi:10.1063/1.3371802

    Google Scholar 

  • Thomassen C (1990) Resistances and currents in infinite electrical networks. J Comb Theory Ser. B 49(1):87–102. doi:10.1016/0095-8956(90)

    Google Scholar 

  • Thovex C, Trichet F (2010) Static and semantic social networks analysis: towards a multidimensional convergent model. In: Proceedings of the 22nd international conference on software engineering and knowledge engineering (SEKE 2010). San Francisco Bay, USA, pp 548–552

  • Wakita K, Tsurumi T (2007) Finding community structure in mega-scale social networks. In: Proceedings of the 16th international conference on world wide web, WWW’07. ACM, New York, pp 1275–1276. doi:10.1145/1242572.1242805

  • Zhuhadar L, Nasraoui O, Wyatt R, Yang R (2011) Visual knowledge representation of conceptual semantic networks. Soc Netw Anal Min 3: 219–299

    Article  Google Scholar 

Download references

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  1. Laboratoire d’Informatique de Nantes Atlantique (UMR-CNRS 6241), LINA, University of Nantes, 2 rue de la Houssiniere, BP 92208-44322, Nantes cedex 03, France

    Christophe Thovex & Francky Trichet

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  1. Christophe Thovex
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  2. Francky Trichet
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Correspondence to Christophe Thovex.

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Thovex, C., Trichet, F. Semantic social networks analysis. Soc. Netw. Anal. Min. 3, 35–49 (2013). https://doi.org/10.1007/s13278-012-0055-y

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