Incomplete and noisy network data as a percolation process

Michael P.H. Stumpf; Carsten Wiuf

doi:10.1098/rsif.2010.0044

Incomplete and noisy network data as a percolation process

Michael P.H. Stumpf, Carsten Wiuf

6 Citationer (Scopus)

Abstract

We discuss the ramifications of noisy and incomplete observations of network data on the existence of a giant connected component (GCC). The existence of a GCC in a random graph can be described in terms of a percolation process, and building on general results for classes of random graphs with specified degree distributions we derive percolation thresholds above which GCCs exist. We show that sampling and noise can have a profound effect on the perceived existence of a GCC and find that both processes can destroy it. We also show that the absence of a GCC puts a theoretical upper bound on the false-positive rate and relate our percolation analysis to experimental protein-protein interaction data.

Originalsprog	Engelsk
Tidsskrift	Journal of the Royal Society Interface
Vol/bind	7
Udgave nummer	51
Sider (fra-til)	1411-1419
Antal sider	9
ISSN	1742-5689
DOI	https://doi.org/10.1098/rsif.2010.0044
Status	Udgivet - 6 okt. 2010
Udgivet eksternt	Ja

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10.1098/rsif.2010.0044

http://rsif.royalsocietypublishing.org/content/7/51/1411.full.pdf

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AU - Stumpf, Michael P.H.

AU - Wiuf, Carsten

PY - 2010/10/6

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N2 - We discuss the ramifications of noisy and incomplete observations of network data on the existence of a giant connected component (GCC). The existence of a GCC in a random graph can be described in terms of a percolation process, and building on general results for classes of random graphs with specified degree distributions we derive percolation thresholds above which GCCs exist. We show that sampling and noise can have a profound effect on the perceived existence of a GCC and find that both processes can destroy it. We also show that the absence of a GCC puts a theoretical upper bound on the false-positive rate and relate our percolation analysis to experimental protein-protein interaction data.

AB - We discuss the ramifications of noisy and incomplete observations of network data on the existence of a giant connected component (GCC). The existence of a GCC in a random graph can be described in terms of a percolation process, and building on general results for classes of random graphs with specified degree distributions we derive percolation thresholds above which GCCs exist. We show that sampling and noise can have a profound effect on the perceived existence of a GCC and find that both processes can destroy it. We also show that the absence of a GCC puts a theoretical upper bound on the false-positive rate and relate our percolation analysis to experimental protein-protein interaction data.

KW - Complex networks

KW - Protein interaction networks

KW - Random graphs

KW - Sampling problems

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DO - 10.1098/rsif.2010.0044

M3 - Review

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VL - 7

SP - 1411

EP - 1419

JO - Journal of the Royal Society. Interface

JF - Journal of the Royal Society. Interface

IS - 51

ER -

Incomplete and noisy network data as a percolation process

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