Skip to main content
Log in

A mathematical model of dynamic social networks

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

A mathematical model for dynamic networks is developed that is based on closed, rather than open, sets. For a social network, it seems appropriate to use a neighborhood concept to establish these sets. We then define a rigorous concept of continuous change, and show that it shares some of the properties associated with the continuity of the calculus. We demonstrate that continuity is local in nature, in that if the network change is discontinuous, it will be so at a single point and the discontinuity will be apparent in that point’s immediate neighborhood. Necessary and sufficient criteria for continuity are provided when the change involves only the addition, or deletion, of individual nodes or connections (edges). To illustrate large scale continuous change, we choose a practical process which reduces a complex network to its fundamental cycles, in the course of which most triadically closed subportions are removed. Finally, we explore several variants of the neighborhood concept, and prove that a rigorous notion of fuzzy closure can be defined.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. In graph theory, Y.η is often called the “open neighborhood of Y” and denoted N(Y), while Y.ρ, denoted N[Y] has been called the “closed neighborhood of Y” (Agnarsson and Greenlaw 2007; Harary 1969). This is a rather different meaning of “closed”.

  2. Because homomorphisms are point functions, not set valued operators, we employ prefix notation.

  3. A graph, or subgraph, is said to be chordal if it contains no cycles of length greater than 3 without a chord (edge) joining two of its points (Jacobson and Peters 1990; McKee 1993).

  4. Figure reprinted with permission from Newman (2006). Copyright (2006) by the American Physical Society.

  5. E.g. the typical \(\epsilon-\delta\) definition of real analysis (Royden 1988).

  6. Many graph theory texts say that Y.η is an “open” neighborhood, c.f (Agnarsson and Greenlaw 2007; Harary 1969).

References

  • Agnarsson G, Greenlaw R (2007) Graph theory: modeling, applications and algorithms. Prentice Hall, Upper Saddle River

  • Ando K (2006) Extreme point axioms for closure spaces. Discrete Math 306:3181–3188

    Google Scholar 

  • Bourqui R, Gilbert F, Simonetto P, Zaidi F, Sharan U, Jourdan F (2009) Detecting structural changes and command hierarchies in dynamic social networks. In: 2009 Advances in Social Network Analysis and Mining, Athens, Greece, pp 83–88

  • Christakis NA, Fowler JH (2009) Connected, the surprising power of our social networks and how they shape our lives. Little Brown & Co., New York

  • Daraganova G, Pattison P, Koskinen J, Mitchell B, Bill A, Watts M, Baum S (2012) Networks and geography: modelling community network structures as the outcome of both spatial and network processes. Social Netw 34:6–17

    Article  Google Scholar 

  • Dekker A (2006) Conceptual distance in social network analysis. J Social Struct 6(3):1–31

    Google Scholar 

  • Edelman PH, Jamison RE (1985) The theory of convex geometries. Geometriae Dedicata 19(3):247–270

    Article  MathSciNet  MATH  Google Scholar 

  • Freeman LC (2000) Visualizing social networks. J Social Struct 1(1):1–19

    Google Scholar 

  • Giblin PJ (1977) Graphs, surfaces and homology. Chapman and Hall, London

  • Harary F (1969) Graph theory. Addison-Wesley, Reading

  • Haynes TW, Hedetniemi ST, Slater PJ (1998) Fundamentals of domination in graphs. Marcel Dekker, New York

  • Hipp JR, Farris RW, Boessen A (2012) Measuring ‘neighborhood’: constructing network neighborhoods. Social Netw 34:128–140

    Google Scholar 

  • Jacobson MS, Peters K (1990) Chordal graphs and upper irredundance, upper domination and independence. Discrete Math 86(1–3):59–69

    Article  MathSciNet  MATH  Google Scholar 

  • Jankovic D, Hamlett TR (1990) New topologies from old via ideals. Am Math Monthly 97(4):295–310

    Article  MathSciNet  MATH  Google Scholar 

  • Koshevoy GA (1999) Choice functions and abstract convex geometries. Math Social Sci 38(1):35–44

    Article  MathSciNet  MATH  Google Scholar 

  • Kossinets G, Watts DJ (2006) Empirical analysis of an evolving social network. Science 311(5757):88–90

    Article  MathSciNet  MATH  Google Scholar 

  • Leskovec J, Lang KJ, Dasgupta A, Mahoney MW (2008) Statistical properties of community structure in large social and information networks. In: WWW 2008, Proceedings of 17th International Conference on the World Wide Web, pp 695–704

  • McKee TA (1993) How chordal graphs work. Bull ICA 9:27–39

    MathSciNet  MATH  Google Scholar 

  • McKee TA, McMorris FR (1999) Topics in intersection graph theory. SIAM Monographs on Discrete Mathematics and Applications. Society for Industrial and Applied Mathematics, Philadelphia

  • Mollenhorst G, Völker B, Flap H (2012) Shared contexts and triadic closure in core discussion networks. Social Netw 34:292–302

    Google Scholar 

  • Monjardet B (2007) Closure operators and choice operators: a survey. In: Fifth International Conference on Concept Lattices and their Applications, Montpellier, France, Oct. 2007. Lecture notes.

  • Monjardet B, Raderinirina V (2001) The duality between the antiexchange closure operators and the path independent choice operators on a finite set. Math Social Sci 41(2):131–150

    Article  MathSciNet  MATH  Google Scholar 

  • Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45:167–256

    Article  MathSciNet  MATH  Google Scholar 

  • Newman MEJ (2006) Finding community structure in networks using the eigenvectors of matrices. Phys Rev E 74(036104):1–22

    Google Scholar 

  • Ore O (1946) Mappings of closure relations. Ann Math 47(1):56–72

    Article  MathSciNet  MATH  Google Scholar 

  • Pfaltz JL (1996) Closure lattices. Discrete Math 154:217–236

    Article  MathSciNet  MATH  Google Scholar 

  • Pfaltz JL (2006) Logical implication and causal dependency. In: Schärfe H, Hitzler P, Øhrstrøm P (eds) Conceptual structures: inspiration and application, volume Springer Verlag LNAI 4068 (supplemental volume), Aalborg University, pp 145–157

  • Pfaltz JL (2008) Establishing logical rules from empirical data. Int J Artif Intell Tools 17(5):985–1001

    Article  Google Scholar 

  • Richards W, Seary A (2000) Eigen analysis of networks. J Social Struct 1(2):1–16

    Google Scholar 

  • Royden HL (1988) Real analysis. Mcmillian, New York

  • Saito A (2010) (ed) Graphs and combinatorics. Springer, Berlin

  • Smyth P (2003) Statistical modeling of graph and network data. In: Proceedings of IJCAI Workshop on Learning Statistical Models from Relational Data, Acapulco, Mexico

  • Šlapal JA (2004) Galois correspondence for digital topology. In: Denecke K, Erné M, Wismath SL (eds) Galois connections and applications. Kluwer Academic, Dordrecht, pp 413–424

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to John L. Pfaltz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pfaltz, J.L. A mathematical model of dynamic social networks. Soc. Netw. Anal. Min. 3, 863–872 (2013). https://doi.org/10.1007/s13278-013-0109-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13278-013-0109-9

Keywords

Navigation