Discriminative topological features reveal biological network mechanisms

Background: Recent genomic and bioinformatic advances have motivated the development of numerous network models intending to describe graphs of biological, technological, and sociological origin. In most cases the success of a model has been evaluated by how well it reproduces a few key features of the real-world data, such as degree distributions, mean geodesic lengths, and clustering coefficients. Often pairs of models can reproduce these features with indistinguishable fidelity despite being generated by vastly different mechanisms. In such cases, these few target features are insufficient to distinguish which of the different models best describes real world networks of interest; moreover, it is not clear a priori that any of the presently-existing algorithms for network generation offers a predictive description of the networks inspiring them. Results: We present a method to assess systematically which of a set of proposed network generation algorithms gives the most accurate des...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Manuel Middendorf, Etay Ziv, Carter Adams, Jen Hom, Robin Koytcheff, Chaya Levovitz, Gregory Woods, Linda Chen, Chris Wiggins
• Keywords: BMCBI 2004, Network Generation, Network Models, Numerous Network Models