Effects of the Awareness-Driven Individual Resource Allocation on the Epidemic Dynamics

Abstract

We investigate the effects of self-protection awareness on the spread of disease from the aspect of resource allocation behavior in populations. To this end, a resource-based epidemiological model and a self-awareness-based resource allocation model in complex networks are proposed, respectively. First of all, we study the coupled disease-awareness dynamics in complex networks with fixed degree heterogeneity. Through extensive Monte Carlo simulations, we find that overall the self-awareness inhibits the spread of disease. More importantly, the influence of the self-awareness on the spreading dynamics can be divided into three phases. In phase I, the self-awareness is relatively small and the outbreak of the epidemic can not be suppressed effectively. While, in phase II, the epidemic size is significantly reduced. Finally, in phase III, there is a sufficiently large value of self-awareness, the disease cannot outbreak anymore. Further, we study the impact of degree heterogeneity on the coupled disease-awareness dynamics and find that the network heterogeneity plays the role of “double-edged sword” in that it can either suppress or promote the epidemic spreading. Specifically, when the basic infection rate is relatively small, it promotes the spread of disease under the condition that there is a relatively small self-awareness. While, when the basic infection rate is relatively large, it inhibits the outbreak of epidemic at a relatively small self-awareness; in turn, it promotes the outbreak of epidemic at a relatively large self-awareness.

Role of degree and weighted coreness based on endpoints in link prediction

“…Link prediction has emerged as a key technical tool for investigating the properties of complicated networks. Link prediction technology can precisely recommend goods in e-commerce networks [1][2][3], recommend friends in social networks [4][5][6], plan routes in transportation networks [7], find the relations between proteins in biological networks [8,9], mine base station information in power networks, [10] and so on. In addition, link prediction can also reveal the structure growth or formation mechanism of complex networks [11,12].…”

The Comprehensive Contributions of Endpoint Degree and Coreness in Link Prediction

“…Furthermore, link prediction algorithms also can be applied to friends' recommendation on online social networks [7][8][9][10][11] and products' recommendation on e-commerce websites [12][13][14][15][16]. Moreover, link prediction algorithms provide references for biological experiments, which can reduce the cost of experiments [17][18][19]. In addition, link prediction algorithms can reveal network evolution mechanism and organization pattern [20][21][22].…”

Link Prediction Based on the Derivation of Mapping Entropy

“…In traditional algorithms, only the degree of endpoint is considered when we evaluate the influence of it. is considers the influences of neighbor nodes to the same extent which loses the impacts of indirect neighbors [16,17]. In fact, due to the different degrees of neighbors, their influence on the endpoint should be different.…”