Having close personal connection to the lab for a few years - He is very successful, very charismatic, and a consummate politician above all. He holds tons of sway over Northeastern admin (lucky to have such a big fish in their little pond), and of course at his Hungarian university he's basically god. Certain policies simply do not apply to him.
Anyway, in the lab there is very strong pressure for things to look right. Some students and postdocs play ball, and enjoy great success in their careers. Some get a little squicked out (use of funds, and the science, seemed to be possible areas of concern), wrap things up quietly and remain on good terms. One or two have major concerns and just kinda disappear. In addition to the treatment of foreigners and management style issues characteristic of academia (which tbh causes more anguish than any of the sketchier stuff).
There are of course a lot of other lazslo affiliated business ventures - startups, books, etc. Right now and for the past few years his big focus has been on producing fine art which can be pretty lucrative, and also .... A good medium for the things he likes to do with money/influence.
Anyway, very nice and charismatic guy, really successful, objectively speaking I admire him, but his name is also used as verb/improper noun in our household if that makes sense
as in "it is very lazslo", "lazslo bs","i lazsloed the fuck out of it" etc. in reference to things such as NFTs, flashy demos built on nothing, marketing driven science, etc.
Interesting critique post, but can you elaborate why you linked the wikipedia bio of a popular scientific podcaster, who apparently has nothing to do with this debate, other than a family connection to an author cited near the end of the post?
I remember Networks and Graphs were all the rage in the early/mid 2010s as everybody was trying to mimic the massive social media successes. This was a very famous book and the author well known during that period. Turns out not everything is a graph or social and lot of that enthusiasm went away.
I would counter that many complex phenomena are best represented by graphs. There's a sort of elbow in the learning curve where you have a lot of tools but as your datasets grow in size the graphs become unmanageable and the signal:noise ratio goes way down. At this point you need to prune or chunk somehow, but it rapidly becomes more of an art than a science and reproducibility is elusive. Advanced techniques disseminate slowly and are not that intuitive, but there's still steady progress, eg deep learning models for sparsification while preserving structure.
Thinking in systems, complex emergent behaviors, networks and graphs, category theory... are all very powerful cognitive tools. They are not explored in basic education and people stumble upon these concepts in college, during masters, and get a boost in their analytical skills. They were all popular objects of popular science writing over different decades and generated some hand wavy "theory of everything" marketing for books. They are quite useful, if a bit overblown in their own little hype context.
I’ve read part of his other book - “The Equation”. Good book and touches on the importance of “networks” in fields like art where you can’t measure quality. Should pick this one up.
I enjoyed the "Networks" book, especially Chapter 0 [sic] where he re-tells how their seminal paper was written.
Overall, I viewed the book as an insightful and inspirational semi-popular science piece that I welcomed. You could view the "network" aspect as a new paradigm that can be applied to many areas, and I'm always keen to hear about such universal ideas.
Now regarding the Nature papers, that's another matter. The criticism should be published in Nature itself, with a response of the original paper's authors. I regret that today it is often paper + online blog response, because not many will easily find the reactions to a paper.
I was just looking at and considering buying "A First Course in Network Science" by Menczer, Fortunato, and Davis. Has anyone gone through that? I'd love to hear a review.
It’s hard to say which book is better, as it depends a lot on what you’re looking for. Menczer’s book does a great job of introducing you to the basic concepts of network theory from a hands on perspective with Python code available through a companion GitHub repo. Barabasi’s book goes into a lot more depth and gives a bit more of an historical perspective of how things developed, but it is significantly more theoretical/mathematical in its presentation.
Full Disclosure I was a co-author of Menczer's, collaborated with his group for several years, and know all the authors personally. I met and spoke with Barabasi multiple times over the years but never worked with him directly.
<ShamelessPlug> you might also want to checkout my substack Graphs for Data Science: https://graphs4sci.substack.com/ where in each post I look at algorithm, implement it in Python, and explore it in a toy problem before showing you how to applying it to a real world dataset. </ShamelessPlug>
Oh wow, hi Bruno (so much for Anon84 haha)! Thanks for the reply. Your book sounds much more like what I'm looking for. I did not know it has Python code to go along with; that's a huge plus for me. Your Substack looks great as well, looking forward to digging into it.
Professional physicist here. I've attended a lecture series by Barabasi in Austria in 2017, I was blown away. After day 1 I ordered the book on Amazon and had it signed before the end of the event.
[1] https://liorpachter.wordpress.com/2014/02/10/the-network-non...
[2] https://en.wikipedia.org/wiki/Andrew_Huberman