Propinquity drives the emergence of network structure and density

Abstract

The lack of large-scale, continuously evolving empirical data usually limits the study of networks to the analysis of snapshots in time. This approach has been used for verification of network evolution mechanisms, such as preferential attachment. However, these studies are mostly restricted to the analysis of the first links established by a new node in the network and typically ignore connections made after each node's initial introduction. Here, we show that the subsequent actions of individuals, such as their second network link, are not random and can be decoupled from the mechanism behind the first network link. We show that this feature has strong influence on the network topology. Moreover, snapshots in time can now provide information on the mechanism used to establish the second connection. We interpret these empirical results by introducing the propinquity model, in which we control and vary the distance of the second link established by a new node, and find that this can lead to networks with tunable density scaling, as found in real networks. Our work shows that sociologically meaningful mechanisms are influencing network evolution and provides indications of the importance of measuring the distance between successive connections.

arXiv comment32 pages, 9 figures, includes Supplementary Information

References in corpus (23)

• Emergence of scaling in random networks
• Modularity and community structure in networks
• The architecture of complex weighted networks
• Evolution of networks
• Network robustness and fragility: Percolation on random graphs
• Resilience of the Internet to random breakdowns
• The Dynamics of Viral Marketing
• Link communities reveal multiscale complexity in networks
• Breakdown of the Internet under intentional attack
• Universality of citation distributions: towards an objective measure of scientific impact
• Network Physiology reveals relations between network topology and physiological function
• Topology of correlation based minimal spanning trees in real and model markets
• How to calculate the fractal dimension of a complex network: the box covering algorithm
• Contact patterns among high school students
• Truncation of power law behavior in "scale-free" network models due to information filtering
• Scaling theory of transport in complex networks
• The Naming Game in Social Networks: Community Formation and Consensus Engineering
• Self-similar scaling of density in complex real-world networks
• Structural Transitions in Dense Networks
• Densification and Structural Transitions in Networks that Grow by Node Copying
• Universal fractal scaling of self-organized networks
• Sparse Maximum-Entropy Random Graphs with a Given Power-Law Degree Distribution
• Revealing effective classifiers through network comparison

Cited by in corpus (2)

• Diversity of information pathways drives scaling and sparsity in real-world networks
• Emergence of Robust and Efficient Networks in a Family of Attachment Models