Working Paper: CEPR ID: DP8807
Authors: Michael D. König; Claudio J. Tessone; Yves Zenou
Abstract: We develop a dynamic network formation model that can explain the observed nestedness in real-world networks. Links are formed on the basis of agents? centrality and have an exponentially distributed life time. We use stochastic stability to identify the networks to which the network formation process converges and find that they are nested split graphs. We completely determine the topological properties of the stochastically stable networks and show that they match features exhibited by real-world networks. Using four different network datasets, we empirically test our model and show that it fits well the observed networks.
Keywords: Nestedness; Bonacich Centrality; Network Formation; Nested Split Graphs
JEL Codes: A14; C63; D85
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| dynamic network formation model (D85) | emergence of nested split graphs (D85) |
| agents' centrality (L85) | structure of the resulting network (D85) |
| linking decisions made by agents based on Bonacich centrality (D79) | network generated by this dynamic process (D85) |
| stochastically stable networks (D85) | properties such as high clustering and short average path lengths (D85) |