This paper investigates the factors influencing the spread of popular topic competition from the perspective of propagation dynamics. The popularity of a topic is directly influenced by the level of people’s interest in it. The rarity, deviation and type can influence the level of interest in such topics. In addition, the topic publisher also has an impact on the competitive dissemination of the topic. The higher the rank of the original publisher is, the easier it spreads. Compared with small bloggers without fans, it is easier to attract people’s attention, and the heat of the topic will rise faster. The results show that the rarer the topic, the greater the deviation from reality, and the more followers the originator has, the more competitive it is.

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The influence of node behavior by the relevant group behavior in complex networks is a topic of recent interest. In order to measure the direct and indirect influence of the neighborhoods, the behavioral propagation and competition model was established based on the pressure. The pressure is described by the impact of group behavior on nodes, which is related to the length and number of reachable paths between two nodes for measuring the nodal behavioral influence. In addition, the pressure range has an effect on the pressure. By modeling and analyzing the change of nodes motivation and the rules of behavioral propagation, and numerical simulations are performed on the small-world networks and the scale-free networks. The results show that pressure is the major factor in the node behavioral motivation, where the pressure generated from behavior in related group network is dependent on the relative location and number of participators. At the same time, network structure also plays an important role at behavior propagation process. Further, competition arises when multiple behaviors are spread among people, while winning behaviors are widely spread among people.

In the research of network structure, long ties are considered to be a hidden but valuable interaction. In this paper, we innovatively interpret the long ties structure in traditional research as a higher-order information spreading path. It effectively avoids the homogenization of information level and redundancy of network structure. The spreading bridge model based on high-order long ties structure is established accordingly. Modern online social behavior is modeled by establishing spreading expansion rate and spreading diffusion rate to improve model adaptability. The spread dynamics model based on multivariate interactions is developed by combining direct-linked spread as a binary interaction relationship in traditional research with bridge-linked spread. The validity of the model is verified by comparing the model simulation results with real cases.