## 2 Related Work  
Opportunistic routing has been extensively studied, and various types of message-forwarding methods and routing algorithms were proposed for \"storage-carry-and-forward\" data transmission with the goal of reducing network overhead and improving data delivery rate. Epidemic Routing [2] flooded the message to all nodes without consideration of routing overhead. Theoretically, Epidemic Routing had the highest success rate of data delivery, but at the price of highest routing overhead. Direct Transmission [3] required that the source stored the data to be sent, and did not forward the data to any nodes but the destination. Direct Transmission had the lowest overhead among all routing algorithms, but also had the lowest success rate of data delivery. Other works in opportunistic routing tried to make a balance between Epidemic Routing and Direct Transmission, and create a possible tradeoff between data delivery rate and routing overhead. [PERSON] et al. [7] used historical data of nodes' encounters and transmission records to compute forwarding probabilities between nodes and the proposed forwarding-probability based routing mechanism PRoPHET, which had the approximate success rate of data delivery of Epidemic Routing [2] with the low routing overhead. [PERSON] et al. [8] took the advantages of Epidemic Routing and Direct Transmission into consideration and proposed a routing scheme called Spray and Wait, which \"sprayed\" a number of copies into the network, and then \"waited\" until one of these nodes met the destination. [PERSON] et al. [9] proposed a delegation forwarding solution for opportunistic routing, which required a node to forward data to only the node with the highest forwarding performance so far. [PERSON] et al. [10] treated opportunistic routing as a resource allocation problem, and each node replicated packets according to packet utility value, which was determined by the delay to destination and encounter probability with destination. [PERSON] et al. [11] proposed a novel mobility prediction-based routing, and the computation of the probability for a node destined to an area was based on the semi-Markov model. In [12; 13; 14], the authors used network coding in opportunistic routing to improve throughput. The above routing schemes used a variety of mechanisms, including discovering the meeting probabilities among nodes, packet utility, and network coding. The primary focus of these mechanisms was to increase the likelihood of finding a path with limited information, and the effectiveness had a relationship with the accuracy of prediction models or evaluation methods. As human beings take part in network activities, the behavior of mobile nodes therefore shows some certain social attributes. Research work [4] had shown that social relationships among nodes could be used to improve the performance of opportunistic routing. Recently, there have been some interesting works based on social relationships among nodes. [PERSON] et al. [15] proposed a social-based routing named BUBBLE. The authors exploited two social and structural metrics, namely, centrality and community. Based on the social activity of nodes, they calculated the global ranking of the nodes in the whole network and the local ranking of the nodes in the community. The message was forwarded to the nodes with high social activity ranking. [PERSON] et al. [16] studied multicast in Delay Tolerant Networks (DTNs) from the social network perspective, used the cumulative probability of node encounter to get node centrality and social community structures, and selected the nodes with high node centrality as the relay nodes in multicast. [PERSON] et al. [17] explored the relationship between geographic and social regularities of human mobility, proposed the concepts of geocommunity and geocentricly, used the semi-Markov process to model user mobility based on the geocommunity structure of the network, and proposed route algorithms to minimize total duration or maximize the dissemination ratio. [PERSON] et al.