Asymmetric flow networks

This paper provides a new model of network formation that bridges the gap between the two benchmark models by Bala and Goyal, the one-way flow model, and the two-way flow model, and includes both as particular extreme cases. As in both benchmark models, in what we call an "asymmetric flow" network a link can be initiated unilaterally by any player with any other, and the flow through a link towards the player who supports it is perfect. Unlike those models, in the opposite direction there is friction or decay. When this decay is complete there is no flow and this corresponds to the one-way flow model. The limit case when the decay in the opposite direction (and asymmetry) disappears, corresponds to the two-way flow model. We characterize stable and strictly stable architectures for the whole range of parameters of this "intermediate" and more general model. We also prove the convergence of Bala and Goyal's dynamic model in this context.

Prosocial norms and degree heterogeneity in social networks

We provide empirical evidence to support the claims that social diversity promotes prosocial behavior. We elicit a real-life social network and its members’ adherence to a social norm, namely inequity aversion. The data reveal a positive relationship between subjects’ prosociality and several measures of centrality. This result is in line with the theoretical literature that relates the evolution of social norms to the structure of social interactions and argues that central individuals are crucial for the emergence of prosocial behavior.

Communities in complex networks

The study of complex networks has attracted the attention of the scientific community for many obvious reasons. A vast number of systems, from the brain to ecosystems, power grid, and the Internet, can be represented as large complex networks, i.e, assemblies of many interacting components with nontrivial topological properties. The link between these components can describe a global behaviour such as the Internet traffic, electricity supply service, market trend, etc. One of the most relevant topological feature of graphs representing these complex systems is community structure which aims to identify the modules and, possibly, their hierarchical organization, by only using the information encoded in the graph topology. Deciphering network community structure is not only important in order to characterize the graph topologically, but gives some information both on the formation of the network and on its functionality.

Exploring the Metrics of the Aquatic Commons repository

17 slide Powerpoint presentation on the deposit and download rates associated with Aquatic Commons from its founding in August 2007 to April 2009.