@inbook{1895383f7aeb4437af8a60e2db4df685,

title = "Network motifs detection using random networks with prescribed subgraph frequencies",

abstract = "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.",

keywords = "Network motifs, Random graphs, Subgraph counting",

author = "Silva, {Miguel E.P.} and Pedro Paredes and Pedro Ribeiro",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.",

year = "2017",

doi = "10.1007/978-3-319-54241-6_2",

language = "English (US)",

series = "Springer Proceedings in Complexity",

publisher = "Springer",

pages = "17--29",

booktitle = "Springer Proceedings in Complexity",

}