Abstract

The analysis of social and technological networks has attracted a lot of attention as social networking applications and mobile sensing devices have given us a wealth of real data. Classic studies looked at analysing static or aggregated networks, i.e., networks that do not change over time or built as the results of aggregation of information over a certain period of time. Given the soaring collections of measurements related to very large, real network traces, researchers are quickly starting to realise that connections are inherently varying over time and exhibit more dimensionality than static analysis can capture.

In this talk we propose new temporal distance metrics to quantify and compare the speed (delay) of information dffusion processes taking into account the evolution of a network from a local and global view. We show how these metrics are able to capture the temporal characteristics of time-varying graphs, such as delay, duration and time order of contacts (interactions), compared to the metrics used in the past on static graphs. We will also describe our study of small world properties of time varying networks.

As a proof of concept we apply these techniques to various time-varying networks, namely connectivity of mobile devices, facebook traces and brain cortical networks.

More information about the work can be found at: http://www.cl.cam.ac.uk/research/srg/netos/spatialtemporalnetworks/