nostr:npub1xtscya34g58tk0z605fvr788k263gsu6cy9x0mhnm87echrgufzsevkk5s
nostr:npub17plqkxhsv66g8quxxc9p5t9mxazzn20m426exqnl8lxnh5a4cdns7jezx0
nostr:npub16lrdq99ng2q4hg5ufre5f8j0qpealp8544vq4ctn2wqyrf4tk6uqn8mfeq
Thx Speaking on stage ! ⚡️🫂
Discussion
was fun! thanks!
By the way, I heard that you were taking photos during nostr:npub1nlnjcakw6xfkpuhx9kym3d20sr774pm6rue5kk93uj7lrca9lypqgqj7fd 's presentation.
I’ve also made a video excerpt of his talk.
Since the presentation was in Japanese,
feel free to ask me if you have any questions.
I can ask him directly.👍
Also, feel free to ask about the presentations of the other speakers as well. 😉
Ah yeah I saw “scale-free” network which caught my interest, since I remember reading up on that at one point.
What interests you specifically about scale-free networks? I may be able to help
I don't remember, I just remembered reading about it at one point
I think it may have been in the context of neural networks or something...
Ah ok, you don't have a specific need? Scale-free networks tend to occur in social networks (probably in Nostr too), so they're important to know about if you want to do network analysis on such a network
What does “scale-free” mean
In the context of a network, "scale-free" means that the degree distribution follows a power law. Degree distribution is math speak for "how many followers does a typical npub have", and a power law is a distribution with so-called fat tails, meaning that some npubs have a huge number of followers, BUT many npubs (the tail) have non-negligible numbers of followers to.