Parsimonious module inference in large networks

Research output: Contribution to journalArticlepeer-review

144 Citations (SciVal)


We investigate the detectability of modules in large networks when the number of modules is not known in advance. We employ the minimum description length principle which seeks to minimize the total amount of information required to describe the network, and avoid overfitting. According to this criterion, we obtain general bounds on the detectability of any prescribed block structure, given the number of nodes and edges in the sampled network. We also obtain that the maximum number of detectable blocks scales as √N, where N is the number of nodes in the network, for a fixed average degree ⟨k⟩. We also show that the simplicity of the minimum description length approach yields an efficient multilevel Monte Carlo inference algorithm with a complexity of O(τNlog⁡N), if the number of blocks is unknown, and O(τN) if it is known, where τ is the mixing time of the Markov chain. We illustrate the application of the method on a large network of actors and films with over 106 edges, and a dissortative, bipartite block structure.
Original languageEnglish
Article number169905
JournalPhysical Review Letters
Publication statusPublished - 5 Apr 2013


Dive into the research topics of 'Parsimonious module inference in large networks'. Together they form a unique fingerprint.

Cite this