Abstract
Information spreading dynamics on the temporal network is a hot topic in the field of network science. In this paper, we propose an information spreading model on an activity-driven temporal network, in which a node is accepting the information dependents on the cumulatively received pieces of information in its recent two steps. With a generalized Markovian approach, we analyzed the information spreading size, and revealed that network temporality might suppress or promote the information spreading, which is determined by the information transmission probability. Besides, the system exists a critical mass, below which the information cannot globally outbreak, and above which the information outbreak size does not change with the initial seed size. Our theory can qualitatively well predict the numerical simulations.
Highlights
Information spreading on social networks is a hot topic in the fields of network science, computer science, and physics [1,2,3,4,5,6,7,8,9]
The spreading size ρ depends on the initial seed size p between the invasion threshold λinv and persistence threshold λpre [52]. e two threshold points can be located by using equation (6). (iii) e third point we investigate in Figure 1 is how ce affects ρ
We find that ρ increases with ce on both temporal and static networks. at is to say, the more homogeneous the degree distribution of the network, the less robust of the network for information spreading. e theoretical predictions agree well with the numerical simulation results. e differences between the theoretical and numerical predictions are induced by the strong dynamical correlations among the states among neighbors
Summary
Information spreading on social networks is a hot topic in the fields of network science, computer science, and physics [1,2,3,4,5,6,7,8,9]. Watts [19] generalized the threshold model to complex networks, and revealed that the information spreading size first increases decreases with the average degree of the network. Discrete Dynamics in Nature and Society model, the study about the effects of network temporality on information spreading dynamics is still lacking. We introduce how the information is spreading on activity-driven temporal networks
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