  Real world networks exhibit rich temporal information: friends are added and
removed over time in online social networks; the seasons dictate the
predator-prey relationship in food webs; and the propagation of a virus depends
on the network of human contacts throughout the day. Recent studies have
demonstrated that static network analysis is perhaps unsuitable in the study of
real world network since static paths ignore time order, which, in turn,
results in static shortest paths overestimating available links and
underestimating their true corresponding lengths. Temporal extensions to
centrality and efficiency metrics based on temporal shortest paths have also
been proposed. Firstly, we analyse the roles of key individuals of a corporate
network ranked according to temporal centrality within the context of a
bankruptcy scandal; secondly, we present how such temporal metrics can be used
to study the robustness of temporal networks in presence of random errors and
intelligent attacks; thirdly, we study containment schemes for mobile phone
malware which can spread via short range radio, similar to biological viruses;
finally, we study how the temporal network structure of human interactions can
be exploited to effectively immunise human populations. Through these
applications we demonstrate that temporal metrics provide a more accurate and
effective analysis of real-world networks compared to their static
counterparts.
