I work in a lab that directly competes with Barabási.
Barabási is seen as a mortal enemy and we try to discredit his work as often as possible, just as this article does.
But the truth is, the whole field is pretty much nonsense.
We try to apply network theory in a way which doesn't really apply. We make assumptions about biology that are trivially false. Any statistical results that reach significance fail to be significant on retesting. Any results which continue to be significant do not match the experimental data.
This field has not yet proven anything of true biological value.
Agreed, sadly. And nice username for the topic at hand - mind shooting me an email? This is a topic near and dear to my heart.
One time I had worked for about a year on some non-trivial enumerative calculations of pathways and flow patterns within metabolic networks. I was due to present these results, pre-publication, as a large multi-departmental talk. The night before the talk I was preparing some visuals and I noticed some oddness in the subnetworks. Upon further investigation I realized I had a bug in my code that reversed a significant number of edges. My conclusions were completely blown and redoing the calculations would take a week at minimum and I couldn't be sure that I would see the same phenomenon in the corrected network. My advisor told me to present that day anyway with mocked visuals and that I would probably get the same result with the real data. I refused on principle but that had repercussions.
Biologists as a whole don't have the best mathematical education and they seem to only appreciate math when it confirms their theories.
What other papers do you consider to be hallmarks of the success of network science from the stat. phys. point of view? The largest critique from non-stat. phys. researchers is that context seems more important than network topology in most applications. Indeed it is important to understand why there is degree heterogeneity. Without understanding the technology surrounding the network however you can not only make incorrect predictions, instead you might be predicting the opposite of reality. http://discovermagazine.com/2007/nov/this-man-wants-to-contr...
Barabási is seen as a mortal enemy and we try to discredit his work as often as possible, just as this article does.
But the truth is, the whole field is pretty much nonsense.
We try to apply network theory in a way which doesn't really apply. We make assumptions about biology that are trivially false. Any statistical results that reach significance fail to be significant on retesting. Any results which continue to be significant do not match the experimental data.
This field has not yet proven anything of true biological value.