A Poisson model for random multigraphs.

ABSTRACT:

Motivation

Biological networks are often modeled by random graphs. A better modeling vehicle is a multigraph where each pair of nodes is connected by a Poisson number of edges. In the current model, the mean number of edges equals the product of two propensities, one for each node. In this context it is possible to construct a simple and effective algorithm for rapid maximum likelihood estimation of all propensities. Given estimated propensities, it is then possible to test statistically for functionally connected nodes that show an excess of observed edges over expected edges. The model extends readily to directed multigraphs. Here, propensities are replaced by outgoing and incoming propensities.

Results

The theory is applied to real data on neuronal connections, interactin

SUBMITTER: Ranola JM 

PROVIDER: S-EPMC3025746 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature