# Social Internet of Vehicles for Smart Cities
## 4 Social Network Analysis in SIoVs
### Centrality of Nodes in an SIoV  
Centrality metrics have been developed and used in the SNA to characterize and measure the importance of individual entities in a social network. The objective of these metrics is to find nodes that are central in a graph and can thus serve as relay nodes in terms of efficient information dissemination. Generally, a node with high centrality is more central compared to its neighbours, although it is highly affected by the network topology and the application that is used. Betweenness centrality is a good metric for identifying nodes that can participate in the forwarding procedure of data [64], especially when used in static networks. According to betweenness centrality, a node is central to the degree that it stands between others. For example, in Figure 2, nodes \(A\) and \(B\) are the most central nodes, because they participate in the majority of shortest paths between the rest of the nodes. Betweenness centrality has been adapted for different types of networks, e.g., sensor networks, where the remaining energy of nodes influences the selection of the next hop in a forwarding chain [65], and delay-tolerant networks, where the delay that every message faces is affected by the popularity of nodes [66]. In mobile _ad hoc_ networks, centrality metrics represent the significance of nodes at any time instance. [PERSON] _et al._[67] show an effective use of social network and centrality analysis in mobile networking. They derive a social routing algorithm from a combination of similarity and betweenness centrality. When the exact location of the destination node is unknown, the algorithm forwards messages to the nearest central node, thereby increasing the potential of selecting a suitable carrier of the information towards the destination. Aside from betweenness centrality, there are similar metrics that combine graph theory with social characteristics. In degree centrality, central actors are the ones that have the most ties in the network graph. Closeness centrality focuses on how close one actor is to the other. The idea is that an actor is central if it can quickly interact with all others, and it is based on the geodesic distances among nodes. Smart vehicles that are equipped with GPS and communication capabilities can make use of closeness centrality to dynamically identify the central nodes that can be good candidates for forwarding important messages [68]. [PERSON] _et al._[69] proposed to rank vehicles based on the road segments that they are going to follow. They start by ranking each road segment according to how many vehicles that enter the network are going to traverse it, based on their historic data. For vehicles with no previous historic data, the shortest path in terms of distance is assumed as the preferred path. Then, each vehicle is assigned a unique ranking based on the accumulated ranking of the roads that it is going to traverse. This metric represents the significance of the node in terms of dissemination capabilities, and it ranks the importance of a node based on dynamic features, e.g., the traffic of road segments and the route of the vehicle. Following a similar approach, [PERSON] and [PERSON] [70] rank vehicles based on the relative location to their neighbours and their potential of reaching other vehicles. Based on these characteristics and inspired by SNA, they propose a new centrality metric, called dissemination capacity. The proposed ReViVprotocol adds a rebroadcaster selection module to the existing IEEE DSRC and thereby reduces the reported delay, so that real-time streaming applications become feasible. The role of central nodes can be played by roadside units (RSUs), which are computing devices located on the roadside that provide connectivity support to passing vehicles. RSUs can be of different kinds, for example cellular base stations or wireless access points, and can provide various kinds of communication capabilities to approaching vehicles.