4 ms·
Gephi is great for exploratory work, but I’ve seen it lure people into using methods that they don’t fully understand. The same problem that many statistics app
by uniqueuid 5y ago
Gephi is great for exploratory work, but I’ve seen it lure people into using methods that they don’t fully understand. The same problem that many statistics apps face.
IMO, a better approach to proper network analysis is to use a library such as the excellent igraph in R or Python together with a clear understanding of the measures.
There is a wonderful book called „network analysis literacy“ by Katharina Zweig which really helps with the latter.
- usgroup 5y agoEvery once in a while I have a network related data science type problem to muddle through and I invariably want to try visualise some aspect of it, so I invariably try use Gephi for it, and it invariably leads to a frustrating experience for anything non trivial, and so I invariably end up doing it in code + graphviz instead.
- uniqueuid 5y agoTo be fair, a large part of the problem is that many graph-related algorithms are simply very expensive (computationally). For example, many layouts (especially classical ones such as Fruchterman-Rheingold) are very slow. That often makes visualization frustrating when you have more than a couple of hundred nodes and/or a dense network. And we should acknowledge that the Gephi people have put a lot of work into making it work. So network analysis evidently benefits from think-then-do approaches, while exploratory work is really hard.
- uniqueuid 5y agoAnd here is the book link: https://link.springer.com/book/10.1007/978-3-7091-0741-6 https://link.springer.com/book/10.1007/978-3-7091-0741-6