Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu


  • Name

    Miguel Pinto Silva
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    10th July 2015


Network motifs detection using random networks with prescribed subgraph frequencies

Silva, MEP; Paredes, P; Ribeiro, P;

Springer Proceedings in Complexity

In order to detect network motifs we need to evaluate the exceptionality of subgraphs in a given network. This is usually done by comparing subgraph frequencies on both the original and an ensemble of random networks keeping certain structural properties. The classical null model implies preserving the degree sequence. In this paper our focus is on a richer model that approximately fixes the frequency of subgraphs of size K - 1 to compute motifs of size K. We propose a method for generating random graphs under this model, and we provide algorithms for its efficient computation. We show empirical results of our proposed methodology on neurobiological networks, showcasing its efficiency and its differences when comparing to the traditional null model. © Springer International Publishing AG 2017.