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Multi-Targets Influence Maximization Algorithm Based on Multi-Cascade Model

机译：基于多级联模型的多目标影响力最大化算法

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摘要

The problem of maximizing personalization influence is to use the online social network as the background to target the specific network users and mine the initial influence communication user set that maximizes the impact of the network users. But existing algorithms still rely on traditional traditional models and cannot imitate real information propagation. On the other hand, the algorithm research on multi-cascade model needs to be expanded. In order to better conform to the information propagation in real life and improve the performance of the algorithm, this paper adopts a multi-cascade model to set the user state to an integer value reflecting the influence quantity, instead of the active or inactive two states in the traditional independent cascade model. Users with the same hobbies tend to get together, so you can use this feature to classify the entire target set using clustering. Based on the above, this paper proposes a multi-targets influence maximization algorithm based on multi-cascade model, clustering candidate users, using clustering center as the seed node to spread information, to maximize the impact on specific users. The intensity of the impact on the target user is measured by the frequency of the individual. The comparison experiments on real social networks show that the personalized impact maximization algorithm based on multi-cascade model has better time performance and propagation effect than the algorithm based on traditional independent cascade model.
机译：最大化个性化影响力的问题是以在线社交网络为背景，以特定网络用户为目标，并挖掘使网络用户的影响力最大化的初始影响力通信用户集。但是现有算法仍然依靠传统的传统模型，无法模仿真实的信息传播。另一方面，需要扩展多级联模型的算法研究。为了更好地适应现实生活中的信息传播并提高算法的性能，本文采用多级联模型将用户状态设置为反映影响量的整数值，而不是有状态或无状态两种状态。在传统的独立级联模型中。具有相同爱好的用户往往会聚在一起，因此您可以使用此功能通过聚类对整个目标集进行分类。在此基础上，提出了一种基于多级联模型的多目标影响力最大化算法，以聚类中心为种子节点进行信息传播，对候选用户进行聚类，以最大程度地提高对特定用户的影响。对目标用户的影响强度是通过个人的频率来衡量的。在真实社交网络上的比较实验表明，与传统的独立级联模型相比，基于多级联模型的个性化影响最大化算法具有更好的时间性能和传播效果。

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