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/pubs-software

  @InProceedings{fogelbergPalade2010a,
  author =       {Christopher Fogelberg and Vasile Palade},
  title =        {GreenSim: A network simulator for comprehensively validating and evaluating new machine learning
                  techniques for network structural inference},
  booktitle =    {IEEE ICTAI2010},
  pages =        {225--230},
  year =         2010,
  volume =       2,
  address =      {Arras, France},
  month =        {October},
  publisher =    {IEEE Computer Society},
  keywords =     {greensim, simulation, evaluation, machine learning, regulatory network, grn}
}

Available here.