A Modified Approach to Inferring Animal Social Networks from Spatiotemporal Data Streams
Animal social networks offer an important research mechanism for animal behaviour analysis. Inferring social network structures in ecological systems from spatiotemporal data streams  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.
KeywordsAnimal social networks Coincident links Spatiotemporal data Fuzzy c-means
- 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
- 4.White, G.C., Garrott, R.A.: Analysis of Wildlife Radio-Tracking Data. Elsevier, Amsterdam (2012)Google Scholar
- 7.Reynolds, D.A.: Gaussian mixture models. Encycl. Biom. 2009, 659–663 (2009)Google Scholar
- 10.Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters (1973)Google Scholar
- 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