## 1 Introduction  
With the mobile cellular network evolving to the fifth generation, huge amounts of information data are generated every day. Opportunistic complex social networks have become a common data delivery platform [1; 2; 3], and the popularity of mobile devices has enabled a variety of new services in social networks to be realized [4]. Many social tools, such as Google Plus [5], Facebook [6], and Twitter [7], have a large number of users and generate data in each moment. The traditional social network approaches to dealing with transmission and reception of big data diversification faces challenges due to the diversification of online data. Many wireless devices used to deliver information and then faced data overload, which would be a hindrance to information interworking and information sharing. In order to cope with the data transmission in 5G wireless networks, we need to design a model and more convenient transmission mode to implement data forwarding in a flexible manner, which is more suitable for complex network environments. In opportunistic social networks, there is no complete end-to-end path between most mobile devices, so nodes communicate by hop by hop. One common method of data dissemination is a base station (BS) that can first deliver a message to a mobile device. The device of the user could carry the message and transmit it to other mobile devices via Device-to-Device(D2D) communication. The mobile device receives data and waits for an \"opportunity\" to deliver the message to the next mobile device [8]. For data forwarding problems, most existing work either assumes that users are completely unselfish, i.e., they are willing to help any user to pass information [9; 10], or assume that users are absolutely selfish and that users need to be encouraged to participate in data transmission [11; 12]. However, in real-life scenarios, users are not absolutely selfish or absolutely selfless. Their level of selfishness may be random or related to social relationships. Therefore, we have established a cooperative relationship to solve this problem. However, the social network is a complex network. The challenge is it is hard to estimate the daily data sent and received by users [13; 14]. It is difficult to satisfy a large amount of data decision, transmission, and storage by a personal PDA device alone. It may cause a low delivery ratio and an excessive energy consumption of the transmission device. The decision could be made by assistance devices, such as the base station or the edge. The delivery ratio can be improved and data transmission energy consumption can be reduced. In addition, this network is divided into multiple disconnected subnets due to the loss of nodes due to problems, such as device movement, failure, or non-cooperation [15; 16]. Such nodes are called critical nodes. The loss of key nodes will cause the network to be disconnected, which leads to the loss of important data in the network [17; 18]. The researcher proposed the concept of probabilistic routing [19]. Each node maintains a link probability table that reaches any other node in the network, and then it could determine the key nodes of message transmission by comparing probability sizes. However, this method is too simple to calculate probabilistic values in the case of an explosion of information. It is also too much trouble to perform calculations and decisions every time the data transmission node performs. We propose a hybrid probability matrix decomposition model to predict the probability of encountering a node. We add node encounter information, social relationships, and partnerships to form a hybrid model to predict the encounter cooperation (EC) values between nodes, and filter the key encounter nodes through EC values. 3. We have designed a simpler way to transfer information to share the large number of transmission tasks of the central equipment. The mobile device only needs to request the central node to encounter the probability table of the other node and the destination node.