Epidemic spreading has been well studied in the past decade, where the main concentration is focused on the influence of network topology but little attention is paid to the individual's crisis awareness. We here study how the crisis awareness, i.e., personal self-protection, influences the epidemic spreading by presenting a susceptible-infected-recovered model with information-driven vaccination. We introduce two parameters to quantitatively characterize the crisis awareness. One is the information creation rate λ and the other is the information sensitivity η. We find that the epidemic spreading can be significantly suppressed in both the homogeneous and heterogeneous networks when both λ and η are relatively large. More interesting is that the needed vaccine will be significantly reduced when the information is well spread, which is a good news for the poor countries and regions with limited resources.
Diffusion of information, behavioral patterns or innovations follows diverse pathways depending on a number of conditions, including the structure of the underlying social network, the sensitivity to peer pressure and the influence of media. Here we study analytically and by simulations a general model that incorporates threshold mechanism capturing sensitivity to peer pressure, the effect of 'immune' nodes who never adopt, and a perpetual flow of external information. While any constant, non-zero rate of dynamically-introduced spontaneous adopters leads to global spreading, the kinetics by which the asymptotic state is approached shows rich behavior. In particular we find that, as a function of the immune node density, there is a transition from fast to slow spreading governed by entirely different mechanisms. This transition happens below the percolation threshold of network fragmentation, and has its origin in the competition between cascading behavior induced by adopters and blocking due to immune nodes. This change is accompanied by a percolation transition of the induced clusters.There are remarkable analogies between the social contagion of information, behavioral patterns or innovation and some physical or epidemic spreading processes, where global phenomena emerge through the diffusion of microscopic states [1][2][3][4]. All evolve in networks with nodes characterized by relevant state variables, and links that represent direct interactions between nodes. In biological systems epidemics are driven by binary interactions that lead to the emergence of simple contagion phenomena [1]. Social diffusion processes are usually characterized by complex contagion mechanisms, where node states are determined by comparing individual thresholds with all neighbor states [2,[5][6][7][8]. This property, capturing the effect of peer pressure and commonly assumed in social spreading phenomena [9,10], has consequences on the dynamics and the final outcome of the social contagion process. Moreover, the theoretical approach to these systems has much in common [1,6,11], which greatly helps us to understand their behavior.Models employing threshold mechanisms mostly focus on cascading phenomena where, under some circumstances, a macroscopic fraction of nodes in the network is converted rapidly due to microscopic * janos. Furthermore, the Watts criterion for macroscopic adoption is purely deterministic, coded in the network structure, threshold distribution and perturbation site -it does not concern time, which is clearly a feature of empirical stochastic processes of adoption spreading.Here we present a general threshold-driven model of social contagion phenomena that captures various spreading scenarios, ranging from cascading behavior to dynamically evolving non-explosive patterns, and sheds light to the different kinetics behind them (Fig. 1) present approximate analytical and numerical results regarding our model. In particular, we study how the kinetics of spreading changes for an increasing density of blocked n...
Real-world networks exhibit prominent hierarchical and modular structures, with various subgraphs as building blocks. Most existing studies simply consider distinct subgraphs as motifs and use only their numbers to characterize the underlying network. Although such statistics can be used to describe a network model, or even to design some network algorithms, the role of subgraphs in such applications can be further explored so as to improve the results. In this paper, the concept of subgraph network (SGN) is introduced and then applied to network models, with algorithms designed for constructing the 1st-order and 2nd-order SGNs, which can be easily extended to build higher-order ones. Furthermore, these SGNs are used to expand the structural feature space of the underlying network, beneficial for network classification. Numerical experiments demonstrate that the network classification model based on the structural features of the original network together with the 1st-order and 2nd-order SGNs always performs the best as compared to the models based only on one or two of such networks. In other words, the structural features of SGNs can complement that of the original network for better network classification, regardless of the feature extraction method used, such as the handcrafted, network embedding and kernel-based methods.
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