🦆 How may network analysis help to understand what happens in biological and ecological systems? Biologist Ferenc Jordán explained in detail the potentialities of the use of a complex network approach in Biology. Read our new blog post:
https://cns.ceu.edu/article/2018-01-15/ecological-networks-individuals-ecosystems

🔖 Gene regulatory network inference: an introductory survey

Vân Anh Huynh-Thu, Guido Sanguinetti

🔗 https://arxiv.org/pdf/1801.04087

📌 ABSTRACT
Gene regulatory networks are powerful abstractions of biological systems. Since the advent of high-throughput measurement technologies in biology in the late 90s, reconstructing the structure of such networks has been a central computational problem in systems biology. While the problem is certainly not solved in its entirety, considerable progress has been made in the last two decades, with mature tools now available. This chapter aims to provide an introduction to the basic concepts underpinning network inference tools, attempting a categorisation which highlights commonalities and relative strengths. While the chapter is meant to be self-contained, the material presented should provide a useful background to the later, more specialised chapters of this book.

💻 https://jaan.io/how-does-physics-connect-machine-learning/

All teaching materials from the 2017 Summer Institute in Computational Social Science videos of lectures, slides & code are available open source:

🔗 https://compsocialscience.github.io/summer-institute/2017/#schedule

✨ https://machinelearningmastery.com/standard-multivariate-multi-step-multi-site-time-series-forecasting-problem/

💲 The #economy includes two flows—wages for work that people use to buy goods and services, and investing that leads to returns. Understanding how these two loops should be balanced can lead to sustained economic growth for all.

http://necsi.edu/research/economics/econuniversal?platform=hootsuite

☸ https://www.quantamagazine.org/simpler-math-tames-the-complexity-of-microbe-networks-20180119/

🕸 شکل‌گیری شبکه‌های کارآمد
http://www.psi.ir/news2_fa.asp?id=2374

مدلی جدید نشان می‌دهد که برای توضیح نحوه تشکیل شبکه‌های عروقی سلسله مراتبی و بهینه‌سازی‌شده، دانستن رشد بافت‌ها حیاتی است، و این درست همانند آن چیزی است که در گیاهان و حیوانات دیده می‌شود.

http://www.psi.ir/upload/news/1396/tavakolidust/961016Images_PhysRevLett_117.png

💡 https://aeon.co/essays/how-a-polymath-transformed-our-understanding-of-information

🔅 Networks and graph theory are beautifully introduced in this package of articles, a great starting point for anyone curious about this exciting part of math and science:
https://plus.maths.org/content/graphs-and-networks

🔖 Random Multi-Hopper Model. Super-Fast Random Walks on Graphs

Ernesto Estrada, Jean-Charles Delvenne, Naomichi Hatano, José L. Mateos, Ralf Metzler, Alejandro P. Riascos, Michael T. Schaub

🔗 arxiv.org/pdf/1612.08631.pdf

📌 ABSTRACT
We develop a model for a random walker with long-range hops on general graphs. This random multi-hopper jumps from a node to any other node in the graph with a probability that decays as a function of the shortest-path distance between the two nodes. We consider here two decaying functions in the form of the Laplace and Mellin transforms of the shortest-path distances. Remarkably, when the parameters of these transforms approach zero asymptotically, the multi-hopper's hitting times between any two nodes in the graph converge to their minimum possible value, given by the hitting times of a normal random walker on a complete graph. Stated differently, for small parameter values the multi-hopper explores a general graph as fast as possible when compared to a random walker on a full graph. Using computational experiments we show that compared to the normal random walker, the multi-hopper indeed explores graphs with clusters or skewed degree distributions more efficiently for a large parameter range. We provide further computational evidence of the speed-up attained by the random multi-hopper model with respect to the normal random walker by studying deterministic, random and real-world networks.

🔅 https://www.johndcook.com/blog/2017/01/04/cover-time-of-a-graph-cliques-chains-and-lollipops/

💻 Fundamentals of Machine Learning is NOW OPEN! Take this excellent tutorial here:

www.complexityexplorer.org/courses/81-fundamentals-of-machine-learning,

and read an interview with the instructors here:

https://www.complexityexplorer.org/news/85-what-s-so-special-about-fundamentals-of-machine-learning

🎞 https://youtu.be/-6INDaLcuJY

🎞 https://www.aparat.com/v/5YC87
https://youtu.be/MOdlp1d0PNA

💻 Great profile about MIT math professor Philippe Rigollet. We have three courses of his on OCW:

Mathematics of Machine Learning:
https://ocw.mit.edu/courses/mathematics/18-657-mathematics-of-machine-learning-fall-2015/

High-Dimensional Statistics:
https://ocw.mit.edu/courses/mathematics/18-s997-high-dimensional-statistics-spring-2015/

Statistics for Applications:
https://ocw.mit.edu/courses/mathematics/18-650-statistics-for-applications-fall-2016/

💡 Complexity is not just a measure of how intricate, or sophisticated, something is. It defines the topology of information flow and of the processes which make Nature work!

🔗 https://resiliencepost.com/2018/01/16/on-the-extraordinary-importance-of-complexity/amp

#Complexity #Entropy #Energy #DNA #Nature