New paper!
How can we detect the presence of communities in networks with higher-order interactions? For instance, by maximizing hypermodularity! Also, this formulation will allow you to leverage tensor spectral methods to do it. Additionally, the paper also argues that the "overfitting" of modularity methods is actually just people applying them where they are not supposed to be used. And, as a byproduct, there is an explanation of why higher-order SVD works so well in classification tasks in machine learning. Oh, the code is available to use in your own projects (link in the first comment). And moreover, the code includes an efficient data structure for higher-order networks that is independent from the community detection method and that you can also use in your own work.

https://journals.aps.org/prresearch/abstract/10.1103/58dr-wktc

Quiz time! What does this chart represent? It’s a timeline from 1977 to 2025. Two clues:

1. It continues to the present day
2. I just watched Wayne’s World 2

#quiz #charts #graphs #timeline #history

Partial alt text: “six colours horizontally, discontinuous, moving without joining — every colour is represented only once at any given time. Exception: There is a gap in every colour at 1986.

All but one line remains continuous from 1987 through the 2010s, with the bottom one continuous from 1991. The bottom two are discontinuous at 2013 and the other four at 2017.”

All those who couldn't join us in Natal for the International School and Workshop in Complex Networks Beyond Pairwise Interactions can still access all the lectures and talks, courtesy of the International Institute of Physics!

- Fundamental definitions and properties
- Dynamical processes
- Synchronisation
- Control
- Belief propagation
- Community detection

and more!

Please boost to reach the largest possible audience.

https://www.youtube.com/playlist?list=PLqTLz9G2bGs2-ToEsAuUg3u6ZASY1UbAk

Generation of four sequences decomposed into weight × level + jump (log(weight), log(level), log(jump)) - three.js animation:

1: The natural numbers (A000027) https://decompwlj.com/3DgraphGen/Natural_numbers.html

Generation of four sequences decomposed into weight × level + jump (log(weight), log(level), log(jump)) - three.js animation:

1: The natural numbers (A000027) https://decompwlj.com/3DgraphGen/Natural_numbers.html

Generation of four sequences decomposed into weight × level + jump (log(weight), log(level), log(jump)) - three.js animation:

1: The natural numbers (A000027) https://decompwlj.com/3DgraphGen/Natural_numbers.html

Generation of four sequences decomposed into weight × level + jump (log(weight), log(level), log(jump)) - three.js animation:

1: The natural numbers (A000027) https://decompwlj.com/3DgraphGen/Natural_numbers.html