title: Computationally efficient measure of topological redundancy of biological and social networks.
abstract: It is well known that biological and social interaction networks have a varying degree of redundancy, though a consensus of the precise cause of this is so far lacking. In this paper, we introduce a topological redundancy measure for labeled directed networks that is formal, computationally efficient, and applicable to a variety of directed networks such as cellular signaling, and metabolic and social interaction networks. We demonstrate the computational efficiency of our measure by computing its value and statistical significance on a number of biological and social networks with up to several thousands of nodes and edges. Our results suggest a number of interesting observations: (1) Social networks are more redundant that their biological counterparts, (2) transcriptional networks are less redundant than signaling networks, (3) the topological redundancy of the C. elegans metabolic network is largely due to its inclusion of currency metabolites, and (4) the redundancy of signaling networks is highly (negatively) correlated with the monotonicity of their dynamics.
authors: Department of Physics, Pennsylvania State University, University Park, Pennsylvania 16802, USA. ralbert@phys.psu.edu
authors: Réka
authors: R
authors: Albert
authors: Bhaskar
authors: B
authors: DasGupta
authors: Rashmi
authors: R
authors: Hegde
authors: Gowri Sangeetha
authors: GS
authors: Sivanathan
authors: Anthony
authors: A
authors: Gitter
authors: Gamze
authors: G
authors: Gürsoy
authors: Pradyut
authors: P
authors: Paul
authors: Eduardo
authors: E
authors: Sontag
mesh_terms: D000818:Animals; D003198:Computer Simulation; D004331:Drosophila melanogaster / Q000166:cytology / Q000235:genetics / Q000378:metabolism; D053263:Gene Regulatory Networks; D060066:Protein Interaction Maps; D015398:Signal Transduction; D060756:Social Networking; D012984:Software