Scale-free networks are rare

A central claim in modern network science is that real-world networks are typically “scale free,” meaning that the fraction of nodes with degree k follows a power law, decaying like k^−α, often with 2<α<3. However, empirical evidence for this belief derives from a relatively small number of real-world networks. We test the universality of scale-free structure by applying state-of-the-art statistical tools to a large corpus of nearly 1000 network data sets drawn from social, biological, technological, and informational sources. We fit the power-law model to each degree distribution, test its statistical plausibility, and compare it via a likelihood ratio test to alternative, non-scale-free models, e.g., the log-normal. Across domains, we find that scale-free networks are rare, with only 4% exhibiting the strongest-possible evidence of scale-free structure and 52% exhibiting the weakest-possible evidence. Furthermore, evidence of scale-free structure is not uniformly distributed across sources: social networks are at best weakly scale free, while a handful of technological and biological networks can be called strongly scale free. These results undermine the universality of scale-free networks and reveal that real-world networks exhibit a rich structural diversity that will likely require new ideas and mechanisms to explain.

 

Scale-free networks are rare
Anna D. Broido, Aaron Clauset

Source: arxiv.org

See Also:

Twitter discussion, including Aaron Clauset, Laszlo Barabasi, Alex Vespignani, Duncan Watts, Stefano Zapperi, Petter Holme, Gabor Vattay, et al.

https://twitter.com/manlius84/timelines/952248309720211458 

Blog post by Petter Holme

https://petterhol.me/2018/01/12/me-and-power-laws/