The concept of temporal networks provides a framework to understand how the interaction between system components changes over time. In empirical communication data, we often detect non-Poissonian, so-called bursty behavior in the activity of nodes as well as in the interaction between nodes. However, such reconciliation between node burstiness and link burstiness cannot be explained if the interaction processes on different links are independent of each other. This is because the activity of a node is the superposition of the interaction processes on the links incident to the node, and the superposition of independent bursty point processes is not bursty in general. Here we introduce a temporal network model based on bursty node activation...
33 pages, 13 figures, 1 table33 pages, 13 figures, 1 table33 pages, 13 figures, 1 table33 pages, 13 ...
Abstract. In temporal networks, both the topology of the underlying network and the timings of inter...
The recent availability of large-scale, time-resolved and high quality digital datasets has allowed ...
Links in many real-world networks activate and deactivate in correspondence to the sporadic interact...
Networks in almost any domain are dynamical entities. New nodes join the system, others leave it, an...
We propose a mathematical description of a dynamic network model in which the number of links fluctu...
We propose a mathematical description of a dynamic network model in which the number of links fluctu...
We introduce a model of adaptive temporal networks whose evolution is regulated by an interplay betw...
The concept of temporal networks is an extension of complex networks as a modeling framework to incl...
Network modeling plays a critical role in identifying statistical regularities and structural princi...
The recent developments in the field of social networks shifted the focus from static to dynamical r...
We introduce a time-varying network model accounting for burstiness and tie reinforcement observed i...
Empirical temporal networks display strong heterogeneities in their dynamics, which profoundly affec...
Burstiness, the tendency of interaction events to be heterogeneously distributed in time, is critica...
Temporal correlations of time series or event sequences in natural and social phenomena have been ch...
33 pages, 13 figures, 1 table33 pages, 13 figures, 1 table33 pages, 13 figures, 1 table33 pages, 13 ...
Abstract. In temporal networks, both the topology of the underlying network and the timings of inter...
The recent availability of large-scale, time-resolved and high quality digital datasets has allowed ...
Links in many real-world networks activate and deactivate in correspondence to the sporadic interact...
Networks in almost any domain are dynamical entities. New nodes join the system, others leave it, an...
We propose a mathematical description of a dynamic network model in which the number of links fluctu...
We propose a mathematical description of a dynamic network model in which the number of links fluctu...
We introduce a model of adaptive temporal networks whose evolution is regulated by an interplay betw...
The concept of temporal networks is an extension of complex networks as a modeling framework to incl...
Network modeling plays a critical role in identifying statistical regularities and structural princi...
The recent developments in the field of social networks shifted the focus from static to dynamical r...
We introduce a time-varying network model accounting for burstiness and tie reinforcement observed i...
Empirical temporal networks display strong heterogeneities in their dynamics, which profoundly affec...
Burstiness, the tendency of interaction events to be heterogeneously distributed in time, is critica...
Temporal correlations of time series or event sequences in natural and social phenomena have been ch...
33 pages, 13 figures, 1 table33 pages, 13 figures, 1 table33 pages, 13 figures, 1 table33 pages, 13 ...
Abstract. In temporal networks, both the topology of the underlying network and the timings of inter...
The recent availability of large-scale, time-resolved and high quality digital datasets has allowed ...