A Modified Approach to Inferring Animal Social Networks from Spatiotemporal Data Streams

  • Pu Zhang
  • Qiang ShenEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 650)


Animal social networks offer an important research mechanism for animal behaviour analysis. Inferring social network structures in ecological systems from spatiotemporal data streams [1] presents a procedure to build such networks based on animal’s foraging process data which consists of time and location records. The method clusters the individuals into different gathering events and links up the individuals that appear in the same events, and subsequently filters coincident links. However, the original model does not perform well in many aspects, including time and space complexity and not-unique coincident link filtering threshold. To modify this method, fuzzy c-means is employed in this work to cluster all links into two groups, strong links or weak links. The work presented here also experimentally compares the performance of the proposed modification against the original method, demonstrating the efficacy of the modified version.


Animal social networks Coincident links Spatiotemporal data Fuzzy c-means 


  1. 1.
    Psorakis, I., Roberts, S.J., Rezek, I., et al.: Inferring social network structure in ecological systems from spatio-temporal data streams. J. Roy. Soc. Interface (2012). rsif20120223Google Scholar
  2. 2.
    Krause, J., Lusseau, D., James, R.: Animal social networks: an introduction. Behav. Ecol. Sociobiol. 63(7), 967–973 (2009)CrossRefGoogle Scholar
  3. 3.
    Aebischer, N.J., Robertson, P.A., Kenward, R.E.: Compositional analysis of habitat use from animal radio-tracking data. Ecology 74(5), 1313–1325 (1993)CrossRefGoogle Scholar
  4. 4.
    White, G.C., Garrott, R.A.: Analysis of Wildlife Radio-Tracking Data. Elsevier, Amsterdam (2012)Google Scholar
  5. 5.
    Whitehead, H., Dufault, S.: Techniques for analyzing vertebrate social structure using identified individuals: review. Adv. Stud. Behav. 28, 33–74 (1999)CrossRefGoogle Scholar
  6. 6.
    Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)CrossRefGoogle Scholar
  7. 7.
    Reynolds, D.A.: Gaussian mixture models. Encycl. Biom. 2009, 659–663 (2009)Google Scholar
  8. 8.
    Moore, D.S., McCabe, G.P.: Introduction to the Practice of Statistics. WH Freeman/Times Books/Henry Holt and Co., New York (1989)zbMATHGoogle Scholar
  9. 9.
    Van der Vaart, A.W.: Asymptotic Statistics. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  10. 10.
    Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters (1973)Google Scholar
  11. 11.
    Bezdek, J.C., Coray, C., Gunderson, R., et al.: Detection and characterization of cluster substructure I. Linear structure: fuzzy c-lines. SIAM J. Appl. Math. 40(2), 339–357 (1981)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Brown, M.S., Pelosi, M.J., Dirska, H.: Dynamic-radius species-conserving genetic algorithm for the financial forecasting of Dow Jones index stocks. In: International Workshop on Machine Learning and Data Mining in Pattern Recognition, pp. 27–41. Springer, Heidelberg (2013)Google Scholar
  13. 13.
    MacKay, D.J.C.: Information Theory, Inference and Learning Algorithms. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  14. 14.
    Shen, Q., Boongoen, T.: Fuzzy orders-of-magnitude-based link analysis for qualitative alias detection. IEEE Trans. Knowl. Data Eng. 24(4), 649–664 (2012)CrossRefGoogle Scholar
  15. 15.
    Su, P., Shang, C., Shen, Q.: Link-based approach for bibliometric journal ranking. Soft. Comput. 17(12), 2399–2410 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Computer Science, Institute of Mathematics, Physics and Computer ScienceAberystwyth UniversityAberystwythUK

Personalised recommendations