I’ve got a fair amount of experience with graph visualizers (worked alongside the team that made the TensorBoard graph visualizer). The SourceCred graph is absolutely enormous and making sense of it “raw” (or even rendering it) will be impossible. That means if we want to use a graph visualizer, we need either:
- To aggressively filter the set of nodes in scope, e.g. show only nodes 1-degree away from a target node. However, it’s easy for a user to be connected to 1k+ nodes, which means using this technique on users (the most interesting nodes) is already a non-starter
- To find some way to “compress” the graph, extracting only salient information
As an example of graph “compression”: maybe we could find a way to collapse the graph down to supernodes and users, while still maintaining meaningful edges. E.g. the “Discourse Artifact” supernode may not be directly connected to @beanow, but it may have a lot of cred paths @beanow. Need to think about the math and probably talk to @mzargham, but I suspect we could do this graph collapse by designating every supernode as a seed, seeing which users cred flows to, and then normalizing by the node’s own cred. (Might need to do this once per time interval, though.)
If that technique worked and we could collapse down to a faithful supernode-and-user map, then we could drive a really interesting graph explorer that represents content at a level of abstraction that’s intelligible to users.
(Note this would only be meaningful for projects that made extensive use of the supernode system… if most of the cred was flowing based on activity, it wouldn’t produce meaningful results.)
We could use a similar technique to do a user->user reduced cred map, which would be great for discovering collaboration patterns (or cliques!).