I studied graph theory heavily in undergrad, started working in a genetics lab, and spent ~7 years in a bioinformatics PhD program focusing on biological networ

I studied graph theory heavily in undergrad, started working in a genetics lab, and spent ~7 years in a bioinformatics PhD program focusing on biological networks. Barabasi was, at one time, the brightest contemporary star in my scientific pantheon. Suffice to say, his publications (and most biological applications of network theory) are fairly meaningless beyond advancing careers. It's a case of cool sounding math that is misused and every PI wants to show that their tiny area of study is also scale-free/subject to cool network effects and therefore worthy of a publication pointing this out. The math is incredibly shoddy all around, and I was burned on several occasions by sticking up for actual scientific principles and pointing out when the math contradicted the assumptions they were trying to prove. As an aside - this is my main distinction between Research and Science. Researchers do an excellent job of confirming their hypotheses via whatever model they can lay their hands on. Scientists falsify their hypotheses until no suitable model is left. I've met a lot of Researchers in my experience but couldn't truthfully tell you a true Scientist since the system discourages their existence. Anyway, my advisor wanted me to SCALE-FREE ALL THE THINGS. The way that everyone gets a power-law in their system is (1) plot their system on a log axis, (2) do a linear fit, (3) voila the slope of the linear approximation is now the exponent of your scale-free power law system, no need to apply any statistical tests to show that your original system is actually exponential! And if you did want to take that extra step, just fiddle with the data until it does fit within your chosen margin of error.