###### Abstract
We define the mobile social network as a network structure that uses mobile devices to communicate and where the nodes in the network have social connections. References [9; 10; 11] point out that the social relationships that exist in human daily life are an important indicator of the performance of opportunistic routing. In the latest research on opportunistic networks, context information, node interests, and node social attributes are often used as variables to measure the performance of opportunistic network routing. The biggest difficulty in opportunistic social networks is the problem of transmission delay and routing overhead when there is a large amount of information that needs to be forwarded. In many scenarios of opportunistic social mobile networks, data or messages take up lots of cache space in mobile equipment. This is because people use mobile devices during data transmission and there is no appropriate node around that can respond in a timely manner, which eventually caused transmission delay. Many existing algorithms choose routing based on the social characteristics of single nodes, and fail to consider that the aggregation of nodes in opportunistic social networks is particularly similar to that of communities in human life, which can reduce a large number of transmission delays and routing overhead. To solve these problems, this work presents an effective data transmission algorithm based on social relationships in opportunistic social Networks (ESR). Because the load capacities of single nodes are limited in the big data environment, we propose a strategy to optimize the information transmission in the opportunistic social network by using the concept of the community [12], because the information transmission strategy through the community is stable, efficient and safe. In this algorithm, we divide the nodes into different communities in the network based on historical connection records [13] and propose a strategy of community structure reduction which divides the networks into several close-knit communities. The contraction of the community structures in the network can ensure fast and efficient information transmission within the community [14] and can also reduce the congestion caused by popular nodes. In the process of information transmission, different numbers of data copies are allocated to each node through communities, which reduces network transmission delay and routing overhead, while improving the success rate of message transmission. The contributions of this paper are listed as follows:  
1. In opportunistic social networks, we propose an effective data transmission algorithm based on social relationships, which divides the network into several communities. By forwarding data through the community structures, the information transfer is faster and safer. 2. After dividing the nodes in the opportunistic social network into communities, we propose a method to reduce the community structure according to the features of nodes, to maintain the cohesion of the community and the high efficiency of data transmission. 3. According to the reduced community structure, we copy the information that needs to be transmitted several times and distribute different numbers of copies to different communities, which can reduce routing overhead while ensuring packet delivery ratio and transmission delay. 4. Using the simulation tool OMNET++, we analyze the performance of the ESR algorithm and compared it with some other algorithms. Our algorithm proposed in this paper has a higher packet delivery ratio, with lower transmission delay and routing overhead. The rest of this paper is structured as follows. In Section 2, we describe and analyze related works. In Section 3, definitions and forwarding methods are presented for the improved Spray and wait algorithm in opportunistic social networks. The simulation results are presented in Section 4. The last section concludes the paper.