Formation of social networks

The Advanced Systems Analysis (ASA) Program in 2013 studied communication, friendship, or trade networks, known as multiplex networks, which form the structural backbone of human societies.

People, social networks © Smitt | iStock

People, social networks

Social networks exhibit scaling laws for several structural characteristics, such as degree of distribution, scaling of the attachment kernel, and clustering of coefficients as a function of node degree. Understanding of possible interrelations between these scaling laws, as well as whether they can be subject to a common dynamical principle can shed light upon mechanisms of social network formation.

Klimek and Thurner [1] proposed a simple model for stationary network formation and apply it to the multiplex data for the friendship, communication and trading networks; they show that the triadic closure could be identified as one of the fundamental dynamical principles in social multiplex network formation.

Szell and Thurner [2] studied gender-specific differences of a multiplex network from a complete behavioral dataset of an online-game society of about 300,000 players. The results confirm quantitatively that females and males manage their social networks in substantially different ways. Among findings are: females perform better economically, are less risk-taking, are more homophile and form more cooperative links than males.

Corominas-Murtra et al. [3] consider an issue of identification of “elites” in social networks, i.e., such subgroups of individuals that influence, lead, govern, and shape societies. Elites are found to be not only composed of highly connected individuals, but also of intermediaries connecting hubs to form a cohesive and structured elite-subgroup at the core of a social network. A generalization of the K-core algorithm was developed to identify those. The approach was validated by data on a virtual society of 420,000 people engaged in a massive multiplayer online game.


[1] Klimek P, Thurner S (2013). Triadic closure dynamics explains scaling-exponents for preferential attachment-, degree- and clustering distributions in social multiplex data. New Journal of Physics, 15, Article #063008.
[2] Szell M, Thurner S (2013). How women organize social networks different from men: gender-specific behavior in large-scale social networks. Scientific Reports 3, Article #1214.
[3] Corominas-Murtra B, Fuchs B, Thurner S (under review). Detection of the elite structure in a virtual multiplex social system by means of a generalized k-core.


ASA’s main collaborators in the field of Formation of social networks include D. Farmer, Professor, Oxford University, UK; J. Geanakoplos, Professor, University of Yale, USA; Y. Holovatch, Professor, National Academy of Science, Ukraine,V. Latora, Professor, Queen Mary's College, London, UK; M.San-Miguel, Professor, Palma, Spain.

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Last edited: 21 May 2014


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