With the popularization of 5G communications, the scale of social networks has grown rapidly, and the types of messages have become increasingly complex. The rapid increases in the number of access nodes and the amount of data have put a greater burden on the transmission of information in the networks. However, when transferring data from a large number of users, the performance of traditional opportunistic network routing algorithms is insufficient, which often leads to problems such as high energy consumption, network congestion, and data packet loss. The way in which to improve this transmission environment has become a difficult task. Therefore, in order to ensure the effective transmission of data and reduce network congestion, this paper proposed a link prediction model based on triangular relationships in opportunistic social networks (LPMBT). In the topological structure of the social network, the algorithm scores links based on the frequency of use and selects the optimal relay node based on the score. It can also efficiently track the target node and reconstruct the sub-community. The simulation experimental results showed that the algorithm had excellent performance, effectively reduced overhead, reduced the end-to-end delay, and greatly improved the data transfer rate in the opportunistic network.

中文翻译：

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机会社交网络中基于三角链接结构预测的数据传输算法

随着5G通信的普及，社交网络的规模迅速增长，消息类型变得越来越复杂。接入节点的数量和数据量的迅速增加给网络中的信息传输带来了更大的负担。但是，当从大量用户那里传输数据时，传统的机会网络路由算法的性能不足，这通常会导致诸如高能耗，网络拥塞和数据包丢失等问题。改善这种传输环境的方法已成为一项艰巨的任务。因此，为了确保数据的有效传输并减少网络拥塞，本文提出了一种基于机会社会网络中三角关系的链接预测模型。在社交网络的拓扑结构中，该算法根据使用频率对链接进行评分，并根据该评分选择最佳中继节点。它还可以有效地跟踪目标节点并重建子社区。仿真实验结果表明，该算法性能优良，有效降低了开销，减少了端到端时延，大大提高了机会网络中的数据传输率。