Complexity Explorer Santa Fe Institute


Analysis of Time-Stamped Data Using Higher-Order Networks

"In a nutshell, we show that the ordering of links in time-stamped data matters. We further introduce higher-order networks, a generalization of the common network perspective that allows to study the influence of order correlations in temporal networks. This new perspective on time-stamped network data provides interesting new opportunities not only to better understand dynamical processes on dynamic networks, but also for the definition of novel information retrieval and community detection algorithms that take into account both the topological and temporal dimension of relational data. In the following, I illustrate some basic concepts of non-Markovian characteristics in temporal networks. In particular, I will showcase the use of pyTempNet, a free python module which simplies the analysis and visualization of time-stamped relational data, as well as the simulation of dynamical processes on top of temporal networks. All of the methods and concepts introduced in the two publications listed above are fully implemented in pyTempNet thus making it particularly easy to apply them to your data."

Ingo Scholtes
Dynamic Systems, Temporal Networks, Python, Network Modeling

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