david on Nostr: Ok, that makes sense. That’s impressive. The neo4j algo that I’m using is ...
Ok, that makes sense. That’s impressive. The neo4j algo that I’m using is iterative. My default personalized PageRank params are 20 iterations, 0.85 damping factor. The neo4j graph data science library is pretty mature and does have an algorithm called eigenvector centrality which I haven’t played with. Could be faster, I don’t know.
I’ve made some measurements on my current implementation. Looks like neo4j takes 3 seconds to project the main graph onto a subgraph and only 2 seconds to run the PageRank algo. Interesting. The graph projection step is only necessary because I’m maintaining a bigger graph that is multipurpose, so those 3 seconds could be shaved off. The rest of the 15 seconds is probably my front end being slow.
Regardless, the linear algebra method is inherently faster than the iterative method, as we’ve discussed.
It’s nice to see stuff starting to get built in this space.
I’ve made some measurements on my current implementation. Looks like neo4j takes 3 seconds to project the main graph onto a subgraph and only 2 seconds to run the PageRank algo. Interesting. The graph projection step is only necessary because I’m maintaining a bigger graph that is multipurpose, so those 3 seconds could be shaved off. The rest of the 15 seconds is probably my front end being slow.
Regardless, the linear algebra method is inherently faster than the iterative method, as we’ve discussed.
It’s nice to see stuff starting to get built in this space.