Evaluating the transport in small-world and scale-free networks

Juárez-López, R.; Obregón-Quintana, Bibiana; Hernández-Pérez, R.; Reyes-Ramírez, Israel; Guzmán‐Vargas, L.

doi:10.1016/j.chaos.2014.09.007

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…For

, the network shows assortative mixing patterns, while for

, it shows dis-assortative mixing trends. For practical purposes, the procedure introduced by Doyle et al [ 51 ] was used to generate three different configurations: (i) assortative, (ii) dis-assortative, and (iii) neutral [ 22 ] (see Figure 1 a–c). Briefly, the nodes are sorted from the highest to the lowest degree.…”

Section: Methods and Modelsmentioning

confidence: 99%

“…In scale-free topologies, the topology of the degree distribution at one scale in the network is self-similar to the degree distribution at other scales, for example, when the degree distribution is a power law. Network models with these distributions have proved valuable in understanding the dynamical processes in digital communications [ 18 ], artificial intelligence [ 19 , 20 ], optimization [ 21 ], transportation [ 22 ], neurology [ 23 ], epidemiology [ 24 ], wireless sensor networks [ 25 ], biology [ 26 , 27 ], and the evolution of cooperative behaviors [ 28 ]. In hierarchical networks, there are clusters of connections within clusters of connections at different scales.…”

Section: Introductionmentioning

confidence: 99%

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Conflict Dynamics in Scale-Free Networks with Degree Correlations and Hierarchical Structure

Jacobo-Villegas

Obregón-Quintana

Guzmán‐Vargas

et al. 2022

Entropy

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We present a study of the dynamic interactions between actors located on complex networks with scale-free and hierarchical scale-free topologies with assortative mixing, that is, correlations between the degree distributions of the actors. The actor’s state evolves according to a model that considers its previous state, the inertia to change, and the influence of its neighborhood. We show that the time evolution of the system depends on the percentage of cooperative or competitive interactions. For scale-free networks, we find that the dispersion between actors is higher when all interactions are either cooperative or competitive, while a balanced presence of interactions leads to a lower separation. Moreover, positive assortative mixing leads to greater divergence between the states, while negative assortative mixing reduces this dispersion. We also find that hierarchical scale-free networks have both similarities and differences when compared with scale-free networks. Hierarchical scale-free networks, like scale-free networks, show the least divergence for an equal mix of cooperative and competitive interactions between actors. On the other hand, hierarchical scale-free networks, unlike scale-free networks, show much greater divergence when dominated by cooperative rather than competitive actors, and while the formation of a rich club (adding links between hubs) with cooperative interactions leads to greater divergence, the divergence is much less when they are fully competitive. Our findings highlight the importance of the topology where the interaction dynamics take place, and the fact that a balanced presence of cooperators and competitors makes the system more cohesive, compared to the case where one strategy dominates.

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“…For

, the network shows assortative mixing patterns, while for

Section: Methods and Modelsmentioning

confidence: 99%