h i g h l i g h t s• A temporal analysis of the popularity dynamics in two online video-provided websites.• Dynamics of the online video popularity can be characterized by the burst behaviors.• The burst behaviors typically occur in the early life span of videos.• Lately the online video popularity restricts to the classic preferential mechanism.Online popularity has a major impact on videos, music, news and other contexts in online systems. Characterizing online popularity dynamics is nature to explain the observed properties in terms of the already acquired popularity of each individual. In this paper, we provide a quantitative, large scale, temporal analysis of the popularity dynamics in two online video-provided websites, namely MovieLens and Netflix. The two collected data sets contain over 100 million records and even span a decade. We characterize that the popularity dynamics of online videos evolve over time, and find that the dynamics of the online video popularity can be characterized by the burst behaviors, typically occurring in the early life span of a video, and later restricting to the classic preferential popularity increase mechanism.

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In a complex environment, how to construct a resilient supply chain network (SCN) that can resist disruptions is a problem of great concern in supply chain management. In order to provide some insights for constructing a resilient SCN, this paper studies the influence of network structures on SCN resilience from the perspective of complex networks. Considering the exit and reselection of enterprises that have been ignored in previous studies, we propose a new SCN model in which nodes are connected with each other based on degree, fitness, and distance. Subsequently, different disruption scenarios are simulated and the resilience of the SCN generated by the proposed model is compared with that of previous models. The simulation results show that the SCN generated by the proposed model is resilient to random disruptions, but vulnerable to targeted disruptions. In particular, the resilience of the SCN will be seriously affected when the strong-strong alliance is broken. Through the research on the influence of the parameters (e.g., α, β, and f) of the proposed model on SCN resilience, we find that α has a significant impact on SCN resilience. The larger the value of α, the worse the resilience of the corresponding SCN. The parameter β has a slight effect on SCN resilience, and the more uniform the distribution of f in the network, the better the resilience of the corresponding SCN. This study may contribute to the design and resilience optimization of SCNs.INDEX TERMS Complex networks, disruptions, network structures, supply chain network resilience.

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