📈 A poll, Graph coloring open-source library in Python, and what does Graph Theory reveal about social media?
Local Optimum: short, imperfect-yet-useful ideas - Edition #5
Welcome to a new edition of Local Optimum: a short, imperfect-yet-useful collection of ideas related to optimization, decision-making, and applied Operations Research.
Let’s dive in! 🪂
1) 📊 A poll: what path…?
I’m curious to know: what path did you take (or plan to take) after studying Operations Research?
There might be several ones, and though I have my own bet, I’d like to better understand the whole picture.
If you want, you can just answer the poll here:
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2) 🎨 Graph coloring open-source library in Python
Do you work on Graph coloring problems?
Existing open-source options for graph colouring are limited.
Current resources tend to either use simple constructive heuristics that lead to low-quality solutions, or exponential-time exact algorithms that cannot cope with larger graphs.
There are also few open-source options for edge colouring, equitable colouring, and solution visualisation, and, to our knowledge, no options for face colouring, weighted colouring, or pre-colouring.
The GCol library features routines for all of the above.
Check it out!
(click on the image below to go to the post)
3) 🌐 What does Graph Theory reveal about social media?
Cristiano Ronaldo, Justin Bieber, Barack Obama, Elon Musk… All of them over 100 million X (formerly Twitter) followers.
Despite their massive followings, none of these mega-accounts appears in the top 20 most-liked tweets. Meanwhile, Macaulay Culkin, with less than a million followers, garnered nearly 3 million likes on a single tweet in 2020.
This fact reveals something interesting: influence has more to do with network position than sheer popularity.
If you’ve ever wondered why some messages go viral and others don’t -or how graphs quietly shape the world around us- this is for you.
Let’s dive back in 👇
Does this journey reflect your own one?
Next Monday, I’ll share my very own journey into optimization.
A path where metaheuristics were my starting point and constant companion.
I’ll cover:
👣 My initial steps into metaheuristics
🎓 Why I decided to pursue a PhD in the field
📚 Key breakthroughs and lessons learned along the way
Whether you're just starting out or already on your optimization journey, I hope my experience offers some valuable insights. See you Monday!
And that’s it for today!
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Have a nice day ahead ☀️
Borja.
