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> At present I am conducting a literature review of approaches to select the right/correct window size for analysing a given longitudinal social network. I did not find many articles in this respect in the present literature. In fact, I did not find any approach/method that can determine an appropriate window size for analysing longitudinal social networks.
> Can anyone help me in this regard!!
You might be interested in this work (about the identification of significant time scales) as well:
Salvatore Scellato, Mirco Musolesi, Cecilia Mascolo and Vito Latora. On Nonstationarity of Human Contact Networks. In Proceedings of the 2nd Workshop on Simplifying Complex Networks for Practitioners (SIMPLEX'10). Colocated with IEEE ICDCS'10. Genova, Italy. June 2010.
Abstract: The measurement and the analysis of the temporal patterns arising in human networks is of fundamental importance to many application domains including targeted advertising, opportunistic routing, resource provisioning (e.g., bandwidth allocation in infrastructured wireless networks) and, more in general, modeling of human social behavior. In this paper we present a novel and exhaustive study of the temporal dynamics of human networks and apply it to different sets of wireless network traces. We consider networks of contacts among users (i.e., peer-to-peer opportunistic networks). We show that we are able to quantify how the amount of information associated to the process evolves over time by using techniques based on time series analysis. We also demonstrate how regular patterns appear only at certain time scales: network dynamics appears nonstationary, in the sense that its statistical description is different at various time scales. These results provide a new methodology to accurately and quantitatively investigate the temporal properties of any type of human interactions and open new directions towards a better understanding of the regular nature of human social behavior.
Link to the paper: http://www.ucl.ac.uk/~ucfamus/papers/simplex10.pdf
Reader in Data Science
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