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Comment by mikkom | Hacker News Reader

Are you working for memgraph perhaps? It certainly seems so based on your posts.

https://news.ycombinator.com/submitted?id=katelatte

Interesting how you got so many upvotes for this content that basically seems a lot like advertisement for memgraphs algorithm.

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Yes, I work for Memgraph, I am a developer there and I wrote this, and all of the previously published articles. I was comparing NetworkX to Memgraph algorithms, since that was the point of the whole article. I am mostly using Python in my day-to-day job and I love what they did with NetworkX. This article was influenced by many people who use NetworkX and are a part of Memgraph community. I just wanted to see how much of a difference does the underlying C++ implementation of Memgraph makes. Since I work with Python tools and Memgraph every day, and talk with a bunch of people working on graph analytics, it makes sense to compare by myself and get the facts right.

Thanks for the context.

Offtopic, does Memgraph have something similar to NetworkX's connected components [1]? Wondering what's the performance difference between both for different sizes of graphs.

[1] https://networkx.org/documentation/stable/reference/algorith...

We do have our own implementation of weakly connected components [1]. Currently, we only have NetworkX strongly connected components algorithm [2] as a part of the nxalg module (set of procedures) in MAGE (our graph algorithms library). I did not compare it yet, let me know if you do! We definitely need to create official benchmarks. Lot of work!

[1] https://memgraph.com/docs/mage/query-modules/cpp/weakly-conn... [2] https://memgraph.com/docs/mage/query-modules/python/nxalg#st...

I forgot to mention: we do have biconnected components algorithm [1], and since all biconnected graphs are strongly connected, it can be useful.

[1] https://memgraph.com/docs/mage/query-modules/cpp/biconnected...

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