Random graph models for directed acyclic networks

Details Random graph models for directed acyclic networks Random graph models for directed acyclic networks

2009-07-24

arxiv.org/abs/0907.4346v1

Phys. Rev. E 80, 046110 (2009)

Physics

Physics and Society

14 pages, 5 figures

Scientific paper

10.1103/PhysRevE.80.046110

We study random graph models for directed acyclic graphs, an important class of networks that includes citation networks, food webs, and feed-forward neural networks among others. We propose two specific models, roughly analogous to the fixed edge number and fixed edge probability variants of traditional undirected random graphs. We calculate a number of properties of these models, including particularly the probability of connection between a given pair of vertices, and compare the results with real-world acyclic network data finding that theory and measurements agree surprisingly well -- far better than the often poor agreement of other random graph models with their corresponding real-world networks.

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