# Inferring Urban Social Networks from Publicly Available Data
### Related Work
#### 1.1.3 Empirical Spatial Networks and Urban Structure  
Spatial social networks have been found to form a large connected component on both mobile phone networks [11; 13; 17] and online social networks [12]. The average degree is typically small, albeit dependent on the type of network considered: it ranges from \(\approx\)5 in mobile phone and online social networks [12; 13; 17], to \(\approx\)10 (when only strong ties are considered) or \(\approx\)20 when the network is inferred from social surveys [60; 61; 62]. Despite the limited average degree, most spatial social networks show the typical small-world effect, with a short average path length close to the well-known \"6 degrees of separation\" rule [11; 12]. Another typical feature of social networks is the high clustering coefficient (i.e., number of triangles) compared to random ER-like networks. Indeed, a clustering value of \(\approx\)0.2 has been found in mobile phone [11; 12] online social [17] and survey-based [19] networks. A clear peculiarity of geographical social networks is instead the absence of very large hubs, which are widely observed across multiple fields, including web graphs [63], the Internet [64], and online social networks [47]. The maximum degree in geographical social networks is apparently limited to a few hundreds, as emerged from both mobile phone networks [11; 13; 18] and the LiveJournal Blog with geo-localization [17], and in line with sociological studies [6; 65; 66]. In References [13; 18], the authors found a power-law distribution with \(\approx\)8 and \(\approx\)5 exponent, respectively, whereas References [11; 17] only highlighted the occurrence of a long-tailed distribution. In Reference [19], the degree distribution is well approximated by a lognormal with \(\sigma=0.9\) and \(\mu=2.6\). A widely studied problem is characterizing the role of spatial proximity and population density in the edge creation process. The dependence of friendship on distance has been found empirically to be an inverse power-law whose exponent \(\beta\) usually lies in the range \([0.5,2]\)[11; 12; 17; 18; 19; 21; 60; 62; 67; 68; 69]. The measured edge length distribution is, however, strongly dependent on the data source and on the size of the considered territory, e.g., it may be almost flat up to 10 km [12] or present a spike for lengths \(<\)5 km [69]. Mobile phone networks, in particular, may cover hundreds or even thousands of kilometers and generally provide geographic locations, and, thus, distances, with a very variable granularity--from 1 km in densely populated urban areas to 10 km in rural regions [11]. Using the network reconstructed from a survey, in Reference [19], the edge length distribution shows two distinct regimes: \(\beta=0.6\) for short range contacts (\(<\)20 km) and \(\beta=1.8\) for long range contacts. In general, a value \(\beta<1\) is not rare [17; 67] and possibly associated with urban networks, as is the case for the value \(\beta=0.44\) obtained for the city of Dublin in Reference [68]. Furthermore, many studies agree on the existence of a significant correlation between geographical proximity and community structure [10; 11; 68; 69; 60]. A recent analysis of the urban area of New York based on the geo-localization of Facebook accounts shows that most of the connections occur between adjacent zip codes and that the underlying transportation infrastructure has a visible impact [46]. Studying a phone call network, however, the authors of Reference [21] found a threshold effect, with a regular increase in the geographical extension of the communities only observable for small clusters with up to 30 members. This was confirmed by a later work, that detected highly clustered connected components spanning across very large areas of the city [11]. Population density is also often considered a factor in the formation of social bonds.