Compression-based inference of network motif sets.

ABSTRACT: Physical and functional constraints on biological networks lead to complex topological patterns across multiple scales in their organization. A particular type of higher-order network feature that has received considerable interest is network motifs, defined as statistically regular subgraphs. These may implement fundamental logical and computational circuits and are referred to as "building blocks of complex networks". Their well-defined structures and small sizes also enable the testing of their functions in synthetic and natural biological experiments. Here, we develop a framework for motif mining based on lossless network compression using subgraph contractions. This provides an alternative definition of motif significance which allows us to compare different motifs and select the coll

SUBMITTER: Benichou A 

PROVIDER: S-EPMC11495616 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature