Network formation
Network formation is an aspect of network science that seeks to model how a network evolves by identifying which factors affect its structure and how these mechanisms operate. Network formation hypotheses are tested by using either a dynamic model with an increasing network size or by making an agent-based model to determine which network structure is the equilibrium in a fixed-size network.
Dynamic models
A dynamic model, often used by
The oldest model of this type is the
One of the most influential models of network formation is the
Agent-based models
The second approach to model network formation is agent- or
Jackson and Wolinsky pioneered these types of models in a 1996 paper, which has since inspired several game-theoretic models.[1] These models were further developed by Jackson and Watts, who put this approach to a dynamic setting to see how the network structure evolve over time.[2]
Usually, games with known network structure are widely applicable; however, there are various settings when players interact without fully knowing who their neighbors are and what the network structure is. These games can be modeled using incomplete information network games.
Growing networks in agent-based setting
There are very few models that try to combine the two approaches. However, in 2007, Jackson and Rogers modeled a growing network in which new nodes chose their connections partly based on random choices and partly based on maximizing their utility function.[3] With this general framework, modelers can reproduce almost every stylized trait of real-life networks.
References
- hdl:10419/221454.
- doi:10.1006/jeth.2001.2903. Archived from the original(PDF) on 2012-07-11.
- .
Further reading
- Barabási and Albert (2002). "Statistical mechanics of complex networks" (PDF). Reviews of Modern Physics. 74 (1): 47–97. doi:10.1103/revmodphys.74.47. Archived from the original(PDF) on 2015-08-24.