Subnets of scale-free networks are not scale-free: sampling properties of networks.

By M. P. Stumpf, C. Wiuf, R. May. Proceedings of the National Academy of Sciences of the United States of America, 2005

578 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/097694ee18f21b7d30e23f7985c3359f77f4d4eb

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Citations in the corpus

Graph theory and networks in Biology. (2006, methodology): “In this subsection, we shall describe the main models that have been used to model biological networks, and some theoretical results on the structure of these models.”
Planetary-scale views on a large instant-messaging network (2008, background): “However, it is known that sampling can corrupt the structural properties of networks, such as the degree distribution and the diameter of the graphs [15].”
New perspectives and methods in link prediction (2010, background): “Furthermore, it is likely that network analogs to common ensemble sampling techniques are fundamentally flawed as a rough corollary of work in [16], where samples of networks with ill-behaving distributions produce new networks with different properties.”
Functional and evolutionary inference in gene networks: does topology matter? (2006, background): “…findings in independent data sets of a power-law distribution of connections, as well as on the extremely low probability of finding such an empirical distribution (even from incomplete and imperfect data) when the underlying network is not scale-free (Yook et al. 2004; but see Stumpf et al. 2005).”
Intrinsic Disorder Is a Common Feature of Hub Proteins from Four Eukaryotic Interactomes (2006, background): “Although differing views regarding the scale-free nature of the protein interaction networks exist [40,41], it is still tempting to speculate that this bias could be explained by a potentially higher fraction of ends as compared with hubs in all genomes.”
A Small World of Neuronal Synchrony (2008, background): “Whereas we had been able to demonstrate this for networks of up to 10 neurons, experimental limitations prevented the tests for the entire networks of up to 24 neurons.”
Biological network comparison using graphlet degree distribution (2007, unknown): “But any short list of properties can easily be mimicked by two very large and different networks.”
Graphlet-based measures are suitable for biological network comparison (2013, background): “It is based on the assumption that the higher the degrees of two nodes, the more likely they are to be neighbours.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “Since complex networks are not homogenous, their segments may display different properties than the whole network (Han et al., 2005; Stumpf et al., 2005; Tanaka et al., 2005; Stumpf & Wiuf, 2010; Annibale & Coolen, 2011; Son et al., 2012).”
Analysis of fungal networks (2012, background): “It is know that topological properties of networks and their samples can differ (Stumpf et al., 2005).”
A benchmarking study of embedding-based entity alignment for knowledge graphs (2020, background): “dataset of RAS is much sparser than the source, because the random sampling cannot retain the connectivity and degree distribution [69].”
Effect of sampling on topology predictions of protein-protein interaction networks (2005, background): “To extrapolate the topology of complete interactomes from such incomplete maps requires the assumption that the limited sampling does not affect the overall topological analyse”
A mixture model for random graphs (2008, background): “This lack of fit has been already analyzed by Stumpf et al. (2005) and Tanaka and Doyle (2005).”