Software Tools and Data
Calculating Betweenness Preference
A fully-documented C# implementation of the betweenness preference measure is available at gitHub.com. The project also includes code to generate time-unfolded representations of temporal networks in tikz or aggregate temporal networks to static graphs. Furthermore it includes documented implementations of all null models used in the paper.
Feel free to use or extend the code and port it to other languages. A tutorial on how to use the tools on your data is available in the gitHub wiki.
Viusalizing Dynamic Networks
A cross-platform Open Source Network Visualization software, which also allows to visualize dynamic networks is available at gitHub as well. The visualization is based on OpenGL and OpenTK.
Dynamic Network Data
An increasing amount of data on the fine-grained dynamics of social, technical and economic systems is becoming available. As a service to our fellow researchers, on this page we collect a number of links to known data sources that are relevant to research on dynamic networks.
MIT Reality Mining: A data set containing time-stamped social interactions recorded by mobile phone proximity sensing technology back in 2004. The data covers a total of 75 individuals tracked over a period of one year. Data is available upon request via the project website.
MIT Reality Mining Data From the Paper: If you want to reproduce the results from the Betweenness Preference paper, we can provide a reduced and processed version of the Reality Mining data (as well as the code used for the preprocessing) upon request. Simply send us an email to firstname.lastname@example.org. In order for us to send these data, you will need to acknowledge that you have obtained the original MIT Reality Mining data (and hence the rights to use it) before (see the link above). Also, you have to cite the original data source when publishing any results utilizing this data.
SocioPatterns: A project that collects time-stamped data on human mobility social interactions by sensing face-to-face interactions via RFID tags. Available data cover dynamic interaction patterns recorded at scientific conferences, exhibitions and primiary schools.