Abstract
Social Network Analysis in the last decade has gained remarkable attention. The current analysis focuses more and more on the dynamic behavior of them. The underlying structure from Social Networks, like facebook, or twitter, can change over time. Groups can be merged or single nodes can move from one group to another. But these phenomenas do not only occur in social networks but also in human brains. The research in neural spike trains also focuses on finding functional communities. These communities can change over time by switching the stimuli presented to the subject. In this paper we introduce a data generator to create such dynamic behavior, with effects in the interactions between nodes. We generate time stamps for events for one-to-one, one-to-many, and many-to-all relations. This data could be used to demonstrate the functionality of algorithms on such data, e.g. clustering or visualization algorithms. We demonstrated that the generated data fulfills common properties of social networks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Akoglu, L., Faloutsos, C.: Rtg: a recursive realistic graph generator using random typing. Data Mining and Knowledge Discovery 19(2), 194–209 (2009)
Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Chakrabarti, D., Faloutsos, C.: Graph mining: Laws, generators, and algorithms. ACM Comput. Surv. 38(1) (June 2006)
Clauset, A., Shalizi, C.R., Newman, M.E.: Power-law distributions in empirical data. SIAM Review 51(4), 661–703 (2009)
Guare, J.: Six Degrees of Separation: A Play. Vintage Books, New York (1990)
Hamming, R.W.: Error detecting and error correcting codes. Bell System Technical Journal 29(2), 147–160 (1950)
Held, P., Kruse, R.: Analysis and visualization of dynamic clusterings. In: 2013 46th Hawaii International Conference on System Sciences (HICSS), pp. 1385–1393. IEEE (2013)
Held, P., Moewes, C., Braune, C., Kruse, R., Sabel, B.A.: Advanced analysis of dynamic graphs in social and neural networks. In: Borgelt, C., Gil, M.Á., Sousa, J.M.C., Verleysen, M. (eds.) Towards Advanced Data Analysis. STUDFUZZ, vol. 285, pp. 205–222. Springer, Heidelberg (2012)
Jaccard, P.: Étude comparative de la distribution florale dans une portion des alpes et des jura. Bulletin del la Société Vaudoise des Sciences Naturelles 37, 547–579 (1901)
Leskovec, J., Chakrabarti, D., Kleinberg, J., Faloutsos, C.: Realistic, mathematically tractable graph generation and evolution, using kronecker multiplication. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 133–145. Springer, Heidelberg (2005)
Louis, S., Borgelt, C., Grün, S.: Generation and selection of surrogate methods for correlation analysis. In: Grün, S., Rotter, S. (eds.) Analysis of Parallel Spike Trains. Springer Series in Computational Neuroscience, vol. 7, pp. 359–382. Springer US (2010)
McGlohon, M., Akoglu, L., Faloutsos, C.: Weighted graphs and disconnected components: Patterns and a generator. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, pp. 524–532. ACM, New York (2008)
Nawrot, M., Aertsen, A., Rotter, S.: Single-trial estimation of neuronal firing rates: from single-neuron spike trains to population activity. Journal of Neuroscience Methods 94(1), 81–92 (1999)
Shalizi, C., Camperi, M., Klinkner, K.: Discovering functional communities in dynamical networks. In: Airoldi, E.M., Blei, D.M., Fienberg, S.E., Goldenberg, A., Xing, E.P., Zheng, A.X. (eds.) ICML 2006. LNCS, vol. 4503, pp. 140–157. Springer, Heidelberg (2007)
Vreeswijk, C.: Stochastic models of spike trains. In: Grün, S., Rotter, S. (eds.) Analysis of Parallel Spike Trains. Springer Series in Computational Neuroscience, vol. 7, pp. 3–20. Springer US (2010)
Watts, D.J., Strogatz, S.H.: Collective dynamics of ’small-world’ networks. Nature 393(6684), 440–442 (1998)
Yule, G.U.: On the association of attributes in statistics: with illustrations from the material of the childhood society, &c. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character 194, 257–319 (1900)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Held, P., Dockhorn, A., Kruse, R. (2014). Generating Events for Dynamic Social Network Simulations. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2014. Communications in Computer and Information Science, vol 443. Springer, Cham. https://doi.org/10.1007/978-3-319-08855-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-08855-6_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08854-9
Online ISBN: 978-3-319-08855-6
eBook Packages: Computer ScienceComputer Science (R0)