Heterogeneous behavioral adoption in multiplex networks

Abstract

Heterogeneity is found widely in populations, e.g. different individuals have diverse personalities and a different willingness to accept novel ideas or behaviors. Whereas population heterogeneity is rarely considered in studying the social contagions on complex networks, especially on multiplex networks. To explore the effect of population heterogeneity on the dynamics of social contagions, a novel model based on double-layer multiplex networks is proposed, in which information diffuses synchronously on the two layers, and each layer is assigned with different adoption thresholds. Meanwhile, populations are classified into the activists and conservatives according to their willingness to adopt new behaviors. To qualitatively understand the effect of population heterogeneity on social contagions, a generalized edge-based compartmental theory is proposed. Through rigorous theoretical analysis and extensive simulations, we find the activists in the two layers promote the adoption of behavior. More interestingly, the crossover phenomena in phase transition are found in the growth of the final adopted size when increasing the information transmission rate. When the proportion of activists is relatively large, the phase transition exhibits continuous pattern. Reducing the proportion of activists induces a hybrid transition, in which the final adoption size first grows continuously at the first threshold, and then increases discontinuously at the second threshold. In addition, we find that the network heterogeneity will change the crossover phenomena in phase transition.

Network public opinion propagation model based on the influence of media and interpersonal communication

“…Therefore, it is important to conduct extensive research on the influence of media and interpersonal relationships on public opinion.In recent times, various models and simulation technologies have become available for the study of public opinion propagation. [3][4][5][6][7][8] Traditionally, the study of public opinion propagation was based on infectious disease models, such as SIS, SIR and SEIR models. [9][10][11] Wang explored the mechanisms of information propagation, including stifling, latent and forgetting mechanisms, to develop a refined SEIR model.…”

Impact of Immunization Strategies on the Dynamics of Social Contagions

“…In the linear threshold model [16,17], the dynamics of social contagions was regarded as a Markov process. In recent years, researchers found that the social reinforcement effect is based on the memory characteristic, and many distinct non-Markovian models [18][19][20][21][22][23][24][25][26][27][28] have been proposed. Wang et al [18] proposed an edge-based compartmental (EBC) theory to qualitatively analyze the steady state of the dynamics of social contagions with nonredundant information memory characteristic and found that the change of some structural parameters (e.g., the heterogeneity of the network structure) or dynamic factors (e.g., the adoption threshold) will cause the crossover phenomenon of the system.…”

Information Spreading on Weighted Multiplex Social Network

“…Previously, various types of social networks such as Facebook [9], Twitter [10], Weibo [11], and Wechat [12] have been used. Social networks are important in information transmission between users [13], such as commodity recommendation [14], news spreading [15,16], advertisement di usion [17,18], health behavior adoption [19], discovery of new friends [20], and social innovation propagation [21]. In addition, understanding the mechanism of information spreading in social networks can facilitate in the design of antivirus strategies [22][23][24], control rumors [25], avoid economic risks [26,27], restrain social unrest [28], etc.…”

“…Wide-ranging factors have been investigated, such as the initial seed size [36], clustering coe cient [37], community structure [38], temporal network [39], role of synergy [40,41], heterogeneous adoption thresholds [42], and limited imitation [43][44][45]. Additionally, information spreading on a multiplex network [46][47][48] has aroused the interest of researchers who perform numerous investigations about communication channel alternations on multiplex networks [49], heterogeneous behavioral adoption on multiplex networks [16], opinion competition on multiplex networks [50], e ect of multiple social networks on user awareness [51], social support for suppressing epidemics on multiplex networks [52], the interplay of social influence in multiplex networks [53], replicator dynamics on multiplex networks [54], etc.…”