Abstract
Records of social interactions provide us with new sources of data for understanding how interaction patterns affect collective dynamics. Such human activity patterns are often bursty, i.e., they consist of short periods of intense activity followed by long periods of silence. This burstiness has been shown to affect spreading phenomena; it accelerates epidemic spreading in some cases and slows it down in other cases. We investigate a model of history-dependent contagion. In our model, repeated interactions between susceptible and infected individuals in a short period of time is needed for a susceptible individual to contract infection. We carry out numerical simulations on real temporal network data to find that bursty activity patterns facilitate epidemic spreading in our model.
Highlights
Communication between individuals is a fundament of human society
We show that bursty activity patterns facilitate epidemic spreading in a variant of the deterministic threshold model [12,13]
We examine spreading dynamics starting from all the possible initial seeds, except for the results shown in Figure 2 for which we select the node with the maximum number of events as the seed
Summary
Communication between individuals is a fundament of human society. Nowadays technologies such as sensor devices and online communication services provide us with records of interaction between individuals, including face-to-face conversations, e-mail exchanges, and phone calls, in massive amounts. Another and richer representation of this type of data is to model them as temporal networks, in which the links between two nodes exist only at the time of an event [2].
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