Literature DB >> 29548211

Synergistic interactions promote behavior spreading and alter phase transitions on multiplex networks.

Quan-Hui Liu1,2,3, Wei Wang1,2,4, Shi-Min Cai1,2,5, Ming Tang1,2,6, Ying-Cheng Lai7.   

Abstract

Synergistic interactions are ubiquitous in the real world. Recent studies have revealed that, for a single-layer network, synergy can enhance spreading and even induce an explosive contagion. There is at the present a growing interest in behavior spreading dynamics on multiplex networks. What is the role of synergistic interactions in behavior spreading in such networked systems? To address this question, we articulate a synergistic behavior spreading model on a double layer network, where the key manifestation of the synergistic interactions is that the adoption of one behavior by a node in one layer enhances its probability of adopting the behavior in the other layer. A general result is that synergistic interactions can greatly enhance the spreading of the behaviors in both layers. A remarkable phenomenon is that the interactions can alter the nature of the phase transition associated with behavior adoption or spreading dynamics. In particular, depending on the transmission rate of one behavior in a network layer, synergistic interactions can lead to a discontinuous (first-order) or a continuous (second-order) transition in the adoption scope of the other behavior with respect to its transmission rate. A surprising two-stage spreading process can arise: due to synergy, nodes having adopted one behavior in one layer adopt the other behavior in the other layer and then prompt the remaining nodes in this layer to quickly adopt the behavior. Analytically, we develop an edge-based compartmental theory and perform a bifurcation analysis to fully understand, in the weak synergistic interaction regime where the dynamical correlation between the network layers is negligible, the role of the interactions in promoting the social behavioral spreading dynamics in the whole system.

Year:  2018        PMID: 29548211     DOI: 10.1103/PhysRevE.97.022311

Source DB:  PubMed          Journal:  Phys Rev E        ISSN: 2470-0045            Impact factor:   2.529


  2 in total

Review 1.  Coevolution spreading in complex networks.

Authors:  Wei Wang; Quan-Hui Liu; Junhao Liang; Yanqing Hu; Tao Zhou
Journal:  Phys Rep       Date:  2019-07-29       Impact factor: 25.600

2.  Modeling competitive evolution of multiple languages.

Authors:  Zejie Zhou; Boleslaw K Szymanski; Jianxi Gao
Journal:  PLoS One       Date:  2020-05-12       Impact factor: 3.240

  2 in total

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