GreenSim: A Network Simulator for Comprehensively Validating and Evaluating New Machine Learning Techniques for Network Structural Inference

Abstract

Networks are very important in many fields of machine learning research. Within networks research, inferring the structure of unknown networks is often a key problem; e.g. of genetic regulatory networks. However, there are very few well-known biological networks, and good simulation is essential for validating and evaluating novel structural inference techniques. Further, the importance of large, genome-wide structural inference is increasingly recognised, but there does not appear to be a good simulator available for large networks. This paper presents GreenSim, a simulator that helps address this gap. GreenSim automatically generates large, genome-size networks with more biologically realistic structural characteristics and 2nd-order non-linear regulatory functions. The simulator itself and the novel method used for generating a network structure with appropriate in- and out-degree distributions may also generalise easily to other types of network. GreenSim is available online at: http://syntilect.com/cgf/pubs:software