Nonrandom behavior in the projection of random bipartite networks.

There are two main categories of networks studied in the complexity physics community: Monopartite and bipartite networks. In this paper, we present a general framework that provides insights into the connection between these two classes. When a random bipartite network is projected into a monopartite network, under quite general conditions, the result is a nonrandom monopartite network, the features of which can be studied analytically. Unlike previous studies in the physics literature on complex networks, which rely on sparse-network approximations, we provide a complete analysis, focusing on the degree distribution and the clustering coefficient. Our findings primarily offer a technical contribution, adding to the current body of literature by enhancing the understanding of bipartite networks within the community of physicists. In addition, our model emphasizes the substantial difference between the information that can be extracted from a network measuring its degree distribution, or using higher-order metrics such as the clustering coefficient. We believe that our results are general and have broad real-world implications.

Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic.

Predicting the evolutionary dynamics of the COVID-19 pandemic is a complex challenge. The complexity increases when the vaccination process dynamic is also considered. In addition, when applying a voluntary vaccination policy, the simultaneous behavioral evolution of individuals who decide whether and when to vaccinate must be included. In this paper, a coupled disease-vaccination behavior dynamic model is introduced to study the coevolution of individual vaccination strategies and infection spreading. We study disease transmission by a mean-field compartment model and introduce a non-linear infection rate that takes into account the simultaneity of interactions. Besides, the evolutionary game theory is used to investigate the contemporary evolution of vaccination strategies. Our findings suggest that sharing information with the entire population about the negative and positive consequences of infection and vaccination is beneficial as it boosts behaviors that can reduce the final epidemic size. Finally, we validate our transmission mechanism on real data from the COVID-19 pandemic in France.