## 1 Introduction  
In recent years, the deployment of mobile devices has become increasingly extensive, and the massive data transmission requirements of mobile devices has put increasing pressure on infrastructure. Emerged from mobile ad hoc networks (MANETs) [1] and the Social Network Service (SNS) [2], opportunistic social networks (OSNs) [3; 4; 5] have been regarded as a complex intermittently connected network architecture, which have been identified as a promising network model to improve network transmission reliability and dissemination environment. OSNs are infrastructure-free: nodes store data and carry it based on the mobility of the underlying user until new communication opportunities arise to forward the data. The store-carry-forward [6; 7] mechanism proposed by OSNs can effectively alleviate the pressure brought by massive data transmission and effectively improve the data deliver ratio. At present, the research direction of OSNs mainly includes routing-forwarding algorithm [8], mobile pattern [9], security and privacy [10], and data storage and management [11]. With the gradual development of 5G networks and big data environments [12; 13], the number of smart mobile devices has increased dramatically, such as Bluetooth, smart phones, laptops or other wearable sensor devices. As a result, the data transmitted over the network link grows exponentially, and the pressure of massive data on network transmission links [14] makes it difficult for some users to enjoy network services conveniently and quickly. Furthermore, the routing overhead of improving infrastructure is getting higher and higher, and it is difficult to meet the demand during large gatherings such as sports events. Besides, in sparsely populated areas, deploying communication infrastructures may have no economic benefit to the operator. Finally, in natural or man-made disasters, infrastructure [15] can be damaged, causing rescue services to be inaccessible to those in need. For the above reasons, OSNs proposed that end-to-end communication connectivity can be established by suitable relay nodes [16] performing store-carry-forward mechanism. The mobile nodes temporarily store the data package in their cache spaces and forward the carried data packages to the peer they encounter. The store-carry-forward mechanism can effectively alleviate network pressure and provide users, with a more comfortable and convenient network service experience. Therefore, it is extremely important for mobile nodes carrying data in OSNs to select relay nodes to perform effective data forwarding process. Especially, how to choose the appropriate relay node to perform data transmission is also a key factor in opportunistic social networks. At present, various routing and forwarding algorithms have been proposed to deal with data dissemination problems in different scenarios. However, most of these algorithms only consider a single factor, either the mobile similarity [17] between nodes or the social similarity [18] of nodes. For algorithms that only consider the mobile similarity of nodes, they typically use the number of real-time common neighbor nodes of the nodes and destination nodes to evaluate the mobile similarity between them. However, the key point that they ignore is that the nodes in the network have certain social relationships with each other, and the social relationships between them can usually be reflected by social attributes [19]. The social attributes of nodes can reflect the special connections between users, which could be termed as a powerful basis for selecting relay nodes. Some studies have shown that nodes with high social similarity are more likely to encounter each other, and thus have more opportunities to transmit data between them. For those algorithms that only consider the social similarity between nodes, they usually use the factor of mutual friends between nodes to evaluate the social similarity between them, but they ignore the importance of mobile similarity of nodes in the process of data transmission.