The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions-modeling your system as temporal networks-can make predictions and mechanistic understanding more accurate. Just as there can be network structures affecting disease spreading, temporal structures can also govern the spreading dynamics. We will discuss recent developments in the analysis of temporal networks, including community detection, the definition of time scales, random walks and various forms of spreading processes. We argue that adding time to network representations fundamentally changes our usual network concepts-so much that it is perhaps meaningless to think of temporal networks as an extension of the network paradigm.
APS March Meeting Abstracts
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