Instead of growing a random network forward according to an evolutionary model, we decompose the actual observed network backwards in time, as dictated by the model,” they say. “The resulting sequence of networks constitute a model-inferred history of the present-day network.” This is network archaeology.
That’s significant because the result depends specifically on the network under investigation, rather than solely on the growth model used to generate it.
They go on to show the power of this idea by inferring the history of several networks. For example, they are able to accurately estimate the time at which users of last.fm joined the network simply by looking at the structure today.
