Latest findings in pre-training graphs and using them for link recommendation Author · Xuzeng He ( ORCID: 0009–0005–7317–7426) Introduction A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of graphs can include social networks (e.g. Instagram) or knowledge graphs (e.g. Wikipedia). In Instagram

I’ve been thinking about something like an instance Community Pledge becoming commonplace. Mastodon instances tend to post rules, user expectations, a tiny bit of info about administrative practices. This helps cultivate the Mastodon region of the fediverse. But, and I don’t mean the following as criticism, most instances have not communicated what their administrative commitments to their community are.

Nat Torkington, picking up on my post over the weekend about the CRU emails takes a slant which has helped me figure out how to write this post which I was struggling with. He says: As I responded over at Radar, yes I am absolutely calling for social software for scientists, but I didn’t mean to say that we could expect it to help us find the visionaries amongst the simply wrong. But this raises a very helpful question.

How do we actually create the service that will deliver on the promise of the internet to enable collaborations to form as and where needed, to increase the speed at which we do science by enabling us to make the right contacts at the right times, and critically; how do we create the critical mass needed to actually make it happen?