The influence maximization problem of a single social network is to find a set of k seed nodes S so that the spread of information from the seed set to the single network has the largest influence. This problem has attracted the attention of many researchers worldwide. In recent years, with the rapid development of the internet and the popularity of social networks, a variety of social platforms have appeared, allowing people to have multiple social accounts simultaneously; that is, one person will participate in multiple social networks and spread information on the various social platforms simultaneously. Consequently, the problem of influence maximization has been extended from a single social network to multiple social networks. However, many studies are based on static networks, and the critical challenge is that social networks usually have dynamic characteristics. At present, there is almost no research on dynamic multiple social networks. Therefore, based on common users, this paper establishes a dynamic multisocial network communication model to study the dynamic multisocial network influence maximization problem (DMNIMP). In this model, multiple dynamic networks are merged into a dynamic network, in which the self-propagating edges of common users are added to the snapshots of each frame of the integrated network. Experimental analysis shows that the proposed model can not only accurately and vividly represent dynamic characteristics but also reflect the mutual influence of common users on multiple social networks. If common users are chosen as the nodes with greater influence in each network, the communication range of the integrated network is obviously larger than that of a single network, and the interaction of dynamic multisocial networks is more obvious.
