Encyclopedia of Social Network Analysis and Mining

Living Edition
| Editors: Reda Alhajj, Jon Rokne

Social Order in Online Social Networks

  • Tina Eliassi-RadEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7163-9_287-1

Glossary

Online Social Network

A social network on the World Wide Web

Social Network

A set of individuals connected by a set of dyadic ties

Tie

A relationship between two individuals

Definition

Social order, a technical term from social sciences (Frank 1944), is the study of how social creatures (such as human beings) are both individual and social (Hechter and Horne 2003). As Hechter and Horne (2003) point out, social order occurs when individuals coordinate and cooperate with each other.

Social order in online social networks and the coordination and cooperation that give rise to them appear in many different structural forms. Examples include homophily, communities (a.k.a. groups), weak ties, structural holes, and social capital.

Homophily The notion of homophily (i.e., “of like attracting like”) has been around since the ancient Greeks. It is often quoted that Plato said, “Similarity begets friendship.” Previous research (McPherson et al. 2001) has shown that homophily is a major criterion governing the formation of ties in social networks. Many social networks have high levels of homophily (Easley and Kleinberg 2010, pp. 79–81). Coordination and cooperation is often more successful between people who are similar to each other – either in terms of status or value (McPherson et al. 2001).

Communities Generally speaking, communities are defined as groups of individuals that are well connected to each other. The existing literature contains many objective functions and algorithms that formalize the aforementioned definition and produce communities (Leskovec et al. 2010). The one pertinent to social order is where a community has low conductance, i.e., where the ratio of ties crossing the community boundary to ties within the community is low (Leskovec et al. 2010). Members of such communities are often tightly connected. These highly connected structures, in turn, promote trust among their members – an important property for social order.

Leskovec et al. (2008) found that the sizes of communities in large online networks roughly follow the Dunbar number (~150) (Dunbar 1998) and that large well-defined communities are absent in online networks. These findings make intuitive sense since maintaining relationships besides the trivial ones requires substantial investment in terms of our neocortex processing capabilities (Dunbar 1998).

Moreover, Leskovec et al. (2008) describe large social networks as having a nested core-periphery structure, where the network is composed of layers of large cores and a small number of dense communities loosely connected to the core. This result indicates the presence of a hierarchy or nested social order in online social networks. In other words, the levels of coordination and cooperation vary depending on where in the nested core-periphery structure a person resides.

Weak Ties Granovetter (2003) was the first to distinguish between weak and strong ties in social networks. He informally defined tie strength as the “amount of time, the emotional intensity, the intimacy (mutual confiding), and the reciprocal services which characterize the tie” (Granovetter 2003, p. 1361). Weak ties correspond to “local bridges” (Easley and Kleinberg 2010), where two people have zero common friends. The lack of common friends can make coordination and cooperation difficult and reduce social order.

Structural Holes Burt (2004) defined structural holes as the empty spaces (i.e., no connections) between groups in the social network. People who fill these structural holes bring social order to the network because they control the information flow and are rewarded with power and wealth.

Social Capital Being members of a community has many advantages (Portes 1998). For example, belonging to a community with high triadic closure (where friend of a friend is a friend) and embeddedness (where two people share many of the same friends) enforces norms and maintains reputational effects. In other words, this “closure” of friends promotes trust. The counterbalance to closure is brokerage. People who are “brokers” interact at the boundary of various communities, i.e., they fill the structural holes. As mentioned above, such people have more social capital compared to others in the community.

Social order, in terms of closures and brokerages, is essential in the preservation of social networks. Closures give rise to communities, while brokerages give rise to connections across various communities.

Cross-References

References

  1. Burt RS (2004) Structural holds and good ideas. Am J Sociol 110(2):349–399CrossRefGoogle Scholar
  2. Dunbar R (1998) Grooming, Gossip, and the evolution of language. Harvard University Press, Cambridge, MAGoogle Scholar
  3. Easley D, Kleinberg J (2010) Networks, crowds, and markets. Cambridge University Press, New YorkCrossRefzbMATHGoogle Scholar
  4. Frank LK (1944) What is social order? Am J Sociol 49(5):470–477CrossRefGoogle Scholar
  5. Granovetter M (2003) The strength of ties. In: Hechter M, Horne C (eds) Theories of social order: a reader. Stanford University Press, Stanford, pp 323–332Google Scholar
  6. Hechter M, Horne C (2003) Theories of social order: a reader. Stanford University Press, StanfordGoogle Scholar
  7. Leskovec J, Lang K, Dasgupta A, Mahoney M (2008) Statistical properties of community structure in large social and information networks. In: The 17th international conference on World Wide Web, Beijing, pp 695–704Google Scholar
  8. Leskovec J, Lang KJ, Mahoney MW (2010) Empirical comparison of algorithms for network community detection. In: The 19th international conference on World Wide Web, Raleigh, pp 631–640Google Scholar
  9. McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Ann Rev Sociol 27:415–444CrossRefGoogle Scholar
  10. Portes A (1998) Social capital: its origins and applications in modern sociology. Ann Rev Sociol 24:1–24CrossRefGoogle Scholar

Recommended Reading

  1. Blau P, Schwartz J (1997) Crosscutting social circles: testing a macro-structural theory of intergroup relations. Transaction Publishers, PiscatawayGoogle Scholar
  2. Burt RS (2005) Brokerage and closure: an introduction to social capital. Oxford University Press, Oxford, UKGoogle Scholar
  3. Gellner E (2003) Trust, cohesion, and the social order. In: Hechter M, Horne C (eds) Theories of social order: a reader. Stanford University Press, Stanford, pp 300–305Google Scholar
  4. Henderon K, Eliassi-Rad T, Papadimitriou S, Faloutsos C (2010) HCDF: a hybrid community discovery framework. In: The 10th SIAM international conference on data mining, Columbus, pp 754–765Google Scholar
  5. Lazarsfeld P, Merton RK (1954) Friendship as a social process: a substantive and methodological analysis. In: Berger M, Abel T, Page CH (eds) Freedom and control in modern society. Van Nostrand, Toronto, pp 18–66Google Scholar
  6. Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci USA 103:8577–8582CrossRefGoogle Scholar
  7. Simmel G (2003) The web of group-affiliations. In: Hechter M, Horne C (eds) Theories of social order: a reader. Stanford University Press, Stanford, pp 316–322Google Scholar
  8. Watts D (2004) Six degrees: the science of a connected age. W.W. Norton & Company, New YorkGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.College of Computer and Information ScienceNortheastern UniversityBostonUSA

Section editors and affiliations

  • Rosa M. Benito
    • 1
  • Juan Carlos Losada
    • 2
  1. 1.Universidad Politécnica de MadridMadridSpain
  2. 2.Universidad Politécnica de MadridMadridSpain