NETWORKS

What Hacker News says

1. teleforceSep 2024
   Please check this seminal textbook on Networks by Mark Newman [1].

   Now you can even query and process network graph data as you would with spreadsheet, relational database and matrices with associate algebra [2].

   [1] Networks [2nd Edition]:

   https://global.oup.com/academic/product/networks-97801988050...

   [2] Mathematics of Big Data: Spreadsheets, Databases, Matrices and Graphs:

   https://mitpress.mit.edu/9780262038393/mathematics-of-big-da...

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2. chermiJun 2024
   Crutchfield has recorded lectures on YouTube. I thought they were quite good. I'm biased toward learning statistical mechanics and nonlinear dynamics as a starting point. Search "phy 256 a(b) crutchfield" on YouTube. Crutchfield has been doing amazing work for quite some time. If you want a critical and very entertaining read, I would check out cosma shalizi's blog. He does a lot of book reviews too that may be helpful. http://bactra.org/weblog/

   Other names/references include "The nature of computation" Stuart kauffman. "Nonlinear dynamics and chaos"

   Too many stat mech books to possibly recommend, for the idea of emergence you'll want something including phase transitions so... Can't go wrong with 'thermodynamics and an introduction to thermostatistics". And for stat mech with lots of complex systems diversions, Sethna's book is excellent but difficult to learn from. "Entropy, complexity, and order parameters. Names in thermo/complexity: Sethna Goldenfeld Parisi HE stanley

   Finally, it's hard to talk about complex systems without talking about networks, "Networks"-Newman is unambiguously the best choice. Names in networks: Mark Newman Reka Albert Avoid barabasi especially more recent stuff.

   There may be better lectures/resources out there I don't know of, but I would start with crutchields lectures. Note again I am coming from heavy physics bias.

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3. epguiDec 2021
   More recently, I've really enjoyed:

   - Options, Futures, and Other Derivatives (John C. Hull): https://www.amazon.ca/Options-Futures-Other-Derivatives-10th...

   - Financial Accounting Theory (Scott & O'Brien): https://www.amazon.ca/Financial-Accounting-Theory-8th-Editio...

   - Networks (Mark Newman): https://www.amazon.ca/Networks-Mark-Newman/dp/0198805098/

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4. epguiDec 2021
   I spend a lot of time sniffing them out.

   One thing I'll do once in a while is visit a university bookstore to get a sense of what's being used in classrooms. I ended up with a lot of my law textbooks like that. In law, I've really loved:

   - Canadian Administrative Law (Guy Régimbald): https://store.lexisnexis.ca/en/categories/shop-by-jurisdicti...

   - Tort Law (Horsey & Rackley): https://www.amazon.ca/Tort-Law-Kirsty-Horsey-dp-019886776X/d...

   - Handbook of Canadian Higher Education Law (Shanahan, Nilso & Broshko): https://www.amazon.ca/Handbook-Canadian-Higher-Education-Law...

   I've been on a maths, stats and ML/AI binge in the last year, and so far my top picks are:

   - Calculus (Stewart): https://www.amazon.ca/Calculus-James-Stewart/dp/1337624187/

   - Linear Algebra Done Right (Sheldon Axler): https://www.amazon.ca/Linear-Algebra-Right-Undergraduate-Mat...

   - An Illustrative Guide to Multivariable and Vector Calculus (Miklavcic): https://www.amazon.ca/gp/product/3030334589/

   - Probability: A Graduate Course (Allan Gut): https://www.amazon.ca/gp/product/1461447070/

   - Statistical Rethinking (McElreath): https://www.amazon.ca/gp/product/036713991X/

   - An Introduction to Statistical Learning (James, Witten, Hastie & Tibshirani): https://www.amazon.ca/Introduction-Statistical-Learning-Appl...

   - Regression and Other Stories (Gelman, Hill & Vehtari): https://www.amazon.ca/gp/product/1107676517/

   - Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman & Hill): https://www.amazon.ca/gp/product/052168689X/

   - Statistical and Data Analysis for Financial Engineering (Ruppert & Matteson): https://www.amazon.ca/gp/product/1493926136/

   - Reinforcement Learning (Sutton & Barto): https://www.amazon.ca/Reinforcement-Learning-Introduction-Ri...

   - Deep Learning (Godfellow, Bengio & Courville): https://www.amazon.ca/Deep-Learning-Ian-Goodfellow/dp/026203...

   - Networks (Mark Newman): https://www.amazon.ca/Networks-Mark-Newman/dp/0198805098/

   I have a ton of textbooks in my wishlist that seem noteworthy and interesting, but that I haven't gotten the chance to read yet: no promises, but you might find something in there https://www.amazon.ca/hz/wishlist/ls/1FWW046Q3RWWM

   My area of expertise is biochemistry, life science, immunology, cell bio... So if anyone wants good recommendations in these topics I have plenty! :)

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5. kwellmanSep 2010
   When the social web came out, I wasn't completely enamored with it as well. It was only later, when I started getting into "network science"[1], and began to view social systems and the internet as large complex systems, that the social web really clicked as something fascinating to get involved with from an engineering perspective.

   Since, as you say, you're not a "particularly social person", maybe if you step back from many of the inanities of the social web and take a big picture view of the system, including the similarities between social systems, biological systems, and other systems, you may gain a new perspective on it.

   [1] See http://www.amazon.com/Networks-Introduction-Mark-Newman/dp/0... for a good introduction.

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