# Improving Traffic Load Distribution Fairness in Mobile Social Networks
## 3 Research Background
### Topology Structures of Mobile Social Networks  
When analysing the delivery performance of a routing algorithm, information of network topology is typically needed. The movement patterns of nodes in mobile networks have a direct impact on the networks' topologies. Mobile social networks, in particular, are human-based networks, and node encounters in such networks represent the ways in which people interact. [PERSON] et al. [38] and [PERSON] et al. [5] studied the topology characteristic of mobile social networks using some realistic human mobility scenarios. They first aggregated the contact data to establish weighted contact graphs, where the link weights express the duration of contact of pairs of nodes. These graphs in turn exhibit the characteristics of social networks (a social network is a graph of human relationships formed by one or more types of interdependencies, such as mutual interests, kinship, or friendship). By applying a complex network analysis on the derived graphs, they concluded that the networks have a non-random (heterogeneous) connectivity structure, exhibiting a power-law degree distribution in which some nodes have a relatively large connectivity degree to other nodes, whereas the majority of nodes in the network have a few. The large degree nodes (so-called hub nodes) are the most popular (central) nodes in the social graph, and therefore they can act as information brokers which are capable of disseminating messages to all nodes within a relatively short delay. In Figure 1 we show the structural topology of a mobile social network: a virtual social network exists on top of a mobile social network, which is less volatile than the physical network, and this network guides humans to move.  
Additionally, we conduct an online analysis in the ONE simulator [22] to investigate the node popularity distribution in mobile social networks. A node in self-organizing networks such as mobile social networks should be able to sense its own popularity throughout the network. Here, node popularity is defined as the number of different nodes contacted in a certain time window. In an aggregated contact graph, this corresponds to node degree (centrality) [21]. In this study, we consider the Reality contact traces [39] as the mobility scenario. In Figure 2 (left) we show the node degree distribution in Reality, where the degree of a node is computed in a 6-h-time-window basis. It is evident that some nodes have a degree value that is significantly larger than the network's average degree (i.e., \(\approx\)2.2). Furthermore, in Figure 2 (right) we show the node degree distribution in the Reality scenario on a log-log scale. The graphic shows that the node degree distribution follows a power-law distribution, with a low probability of finding nodes with a high degree because most network nodes have a low one. Moreover, the authors of [40] established the potential of coupling between mobile social networks and scale-free networks, which have a power-law degree distribution as their main characteristic. In other words, the degree distribution in real human-based networks differs from the Gaussian (normal) degree distribution commonly assumed in random networks.  
Figure 1: A mobile social network’s structural topology. On the top layer, the social network drives human to move, and this human mobility creates opportunistic contacts in the physical network.