Analysis of Ego Network Structure in Online Social Networks

Abstract

Abstract-Results about offline social networks demonstrated that the social relationships that an individual (ego) maintains with other people (alters) can be organised into different groups according to the ego network model. In this model the ego can be seen as the centre of a series of layers of increasing size. Social relationships between ego and alters in layers close to ego are stronger than those belonging to more external layers. Online Social Networks are becoming a fundamental medium for humans to manage their social life, however the structure of ego networks in these virtual environments has not been investigated yet. In this work we contribute to fill this gap by analysing a large data set of Facebook relationships. We filter the data to obtain the frequency of contact of the relationships, and we check -by using different clustering techniques -whether structures similar to those found in offline social networks can be observed. The results show a strikingly similarity between the social structures in offline and Online Social Networks. In particular, the social relationships in Facebook share three of the most important features highlighted in offline ego networks: (i) they appear to be organised in four hierarchical layers; (ii) the sizes of the layers follow a scaling factor near to three; and (iii) the number of active social relationships is close to the well-known Dunbar's number. These results strongly suggest that, even if the ways to communicate and to maintain social relationships are changing due to the diffusion of Online Social Networks, the way people organise their social relationships seems to remain unaltered.

Limitations of time resources in human relationships determine social structures

“…In the six communication systems examined in this study, the distributions of the strengths of social relationships d showed power law distributions (Fig. 1), similar to those observed in previous studies (Arnaboldi et al, 2012;Song et al, 2012;Fujihara and Miwa, 2014;Hossmann et al, 2011;Hu et al, 2012). Individuals selected social grooming partners in proportion to the strength of their social relationships, that is, the individuals tended to reinforce their strong social relationships; these power law distributions were generated by the Yule-Simon process (Yule, 1925;Simon, 1955;Newman, 2005) (Fig.…”

“…O n the basis of the social brain hypothesis (Byrne and Whiten, 1989;Whiten and Byrne, 1997;Dunbar, 2000;Dunbar, 2003), an explanation of social structures, which are typical for humankind, is expected to offer knowledge about human origins, such as the limitation on the number of social relationships (Dunbar, 2000;Dunbar, 2003;Hill and Dunbar, 2003;Gonçalves et al, 2011;Arnaboldi et al, 2013a) and the skewness of social relationships (Zhou et al, 2005;Hossmann et al, 2011;Arnaboldi et al, 2012;Hu et al, 2012;Song et al, 2012;Arnaboldi et al, 2013b;Fujihara and Miwa, 2014), that is, its distributions following a power law (Hossmann et al, 2011;Arnaboldi et al, 2012;Hu et al, 2012;Pachur et al, 2012;Song et al, 2012;Fujihara and Miwa, 2014). These social structures may be caused by human beings' social grooming strategies (Dunbar, 2004).…”

“…Thus, we expect that they employ strategies to distribute the limited time resources to maximize benefits from their social relationships (Brown and Brown, 2006;Miritello et al, 2013b;Saramaki et al, 2014). As a result of such strategies, social relationship strengths (as measured by frequency of social grooming (Roberts and Dunbar, 2011;Arnaboldi et al, 2012;Song et al, 2012;Arnaboldi et al, 2013b;Fujihara and Miwa, 2014;Saramaki et al, 2014) may often show a much skewed distribution (Zhou et al, 2005;Arnaboldi et al, 2013b) (distributions following a power law (Hossmann et al, 2011;Arnaboldi et al, 2012;Hu et al, 2012;Song et al, 2012;Fujihara and Miwa, 2014)). …”

“…They are useful for constructing theoretical models of social phenomena. Studies (Arnaboldi et al, 2012;Song et al, 2012;Fujihara and Miwa, 2014;Hossmann et al, 2011;Arnaboldi et al, 2013b;Hu et al, 2012;Takano et al, 2016a;Saramaki et al, 2014;Dunbar, 2016;Takano et al, 2016b) previously cited in this paper have also analysed data from SNSs, mobile phones, social network games, and SMSs.…”

Neighbourhood-Pair Attack in Social Network Data Publishing

“…This problem has recently received considerable traction as more and more social network data has been released publicly to enable social network data analysis [1]. Currently, the privacy of individuals and the confidentiality of data in social network data publishing are protected through variety anonymization techniques.…”