Abstract
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 and show that it leads to heavy-tailed inter-event time distributions for both node dynamics and link dynamics. Our analysis indicates that activation processes intrinsic to nodes give rise to dynamical correlations across links. Our framework offers a way to model competition and correlation between links, which is key to understanding dynamical processes in various systems.
Highlights
Temporal networks have become an important framework to understand the dynamics of complex systems over the past decade [1,2,3]
We show that the number of interactivation time (IAT) that compose each intercommunication time” (ICT) is a random variable that follows a power-law distribution
To explain the origin of the bursty activity patterns of nodes and links observed in empirical communication systems, we have proposed a temporal network model in which the nodes communicate with each other according to their nonPoissonian random activation
Summary
Temporal networks have become an important framework to understand the dynamics of complex systems over the past decade [1,2,3]. The interaction dynamics can be captured at several different levels. The interaction between each pair of nodes specifies the dynamics of the link. By aggregating the interaction between a node and all of its neighbors, one obtains the dynamics of the node, which shows how the node interacts with others. By collecting all the interaction between every pair of nodes, one can tell about the dynamics of the entire system. In communication systems in which people interact by sending messages, the link dynamics corresponds to the message correspondence between a pair of individuals, while the node dynamics corresponds to the inbox of messages sent or received by an individual
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