## 2 Related Work
[18] took the use of frequency and duration of node contacts to generate the social graph,addressed the community evolution problem, proposed distributed algorithms based on the social graph to detect the overlapping communities and bridge nodes, and designed a social-based routing scheme. [PERSON] et al. [19] studied cluster based routing, and proposed a routing scheme with cluster size adjusted dynamically. [PERSON] et al. [20] developed the PeopleRank approach for node ranking, which was similar to PageRank. PeopleRank gave higher weight to nodes that were socially connected to other important nodes of the network. In message forwarding, a node \(u\) forwarded a message to a node \(v\) that it encountered if the rank of \(v\) was higher than the rank of \(u\). [PERSON] et al. [21] proposed a socially aware and stateless routing scheme called SANE, in which message forwarding was based on the interest similarity between nodes. SANE had the advantage of requiring less of a buffer of nodes than other approaches. However, the interest is just one of the social ties between nodes. For the nodes having no common interest, but frequent contact with the destination, they can often make a great contribution to message delivery, as is not considered in SANE. From different viewpoints of social relationships among nodes, the above works proposed the effective routing solutions for a variety of problems, which had shown that social relationships among nodes had a major impact on the performance of opportunistic routing. With the growing popularity of smartphones, more and more people have the opportunity to participate in a variety of applications of MONs, and comprehensive study of the social relationships among nodes will be more important than before in MONs. Different to the existing research work, in this paper, we take a real social phenomenon as an example to discuss the measurement methods of social relationships, and propose a novel social-based scheme for clustering and routing in MONs. In our daily lives, we can get a lot of information from the circles of friends in social communication networks such as Facebook, Twitter, Wechat, and so on, which inspires us to analyze the social relationships among nodes in MONs. In the real world, the social relationship between two people is not only shown in direct contact with each other, but also in their common circles of friends. For example, in our lives, there is often such a phenomenon: [PERSON] has two grandmothers \(D\) and \(E\): \(D\) is the mother of [PERSON]'s mother, and \(E\) is the mother of [PERSON]'s father. Although \(D\) and \(E\) are close relatives, they may never contact each other because of their opposing personalities. In this phenomenon, there is no direct contact between \(D\) and \(E\), but, in fact, they have strong social relationships because they have a common \"circles of friends\". As the saying goes, \"like attracts like\", and we have many types of circles of friends such as working circles, classmate circles, common hobby circles and so on. Real-life experience tells us that we can get important information from circles of our friends. In an MON, the nodes are often wireless devices carried by human beings, and thus the activities of nodes are inevitable for showing some social characteristics described above. Consequently, the study of social relationships should reveal the explicit relationships among nodes via direct contact events, and also determine the implicit social relationships among them from their circles of friends. In this paper, we divide the social relationships between two nodes into two parts. One part comes from direct contact events, which are called Explicit Social Relationships (ExSRs). Another part of relationships should be relayed by their common friends, called Implicit Social Relationships (ImSRs). We present the measure methods of ExSRs and ImSRs, which are used in the design of clustering and routing.