I worked in a startup whose value proposition was largely derived from using graph data and graph databases under the hood. The main benefit is, as the repo even states "Fast data processing: Links traversal is processed with O(1) complexity."

So, technically, you can do deep traversals quicker. A few notes:

1. Few use cases truly need low-latency deep traversal on realtime data (>5 hops). There are some well known ones like fraud detection in payment processing and, possibly, social media recommendation engines. But I am not even sure how latest social media engines work, and whether they still rely on graph DBs.

2. However, in practice the advantage is often marginal. With modern analytical databases, or even an optimized PostgreSQL (ltree, materialized views, pgrouting, pg_duckdb, etc), you often get more than good enough performance. In addition those traditional SQL DBs scale with hardware more easily than graph databases. So, you can always use the lever: "Throw more hardware at it."

3. Even Graph DBs don't get good traversal performance under all conditions without hand tuning. For example, there is the "super node" issue, a node with an abnormally high number of connections (edges). And if you still need hand-tuning, you might as well choose something more versatile.

4. The ecosystem of a PostgreSQL and other popular DBs is just unbeaten. With graph DBs, you often prematurely put yourself in a corner that you don't want to be in.

Hence, my recommendation. Unless you are really sure that a graph DB is the right fit for your use case, start with something else, and go the graph db way when you have established a true need.

I'm a bit of a Forensic Files nerd, so I worked on a little side project a while back to pull the transcripts from several episodes, use entity recognition to categorize people, places, things, etc, and load them into a Neo4j database (via Cypher queries). It turns out there's something called the POLE data model[1] that can be used by law enforcement to help solve crimes. You load all the details into a graph database and evaluate the relationships to aid in solving crimes. I suppose you could argue a criminal investigation is essentially graph traversal.

[1] https://neo4j.com/blog/government/graph-technology-pole-posi...

Edit: typo

Most graph databases only offer an advantage in query language, as it allows for more ergonomic graph traversal compared to e.g. recursive CTEs in SQL. However with SQL/PGQ, the same query ergonomics are coming to traditional databases.

If you look under the hood there is usually nothing special about graph databases that will make them more performant. If you lay out a query plan side-by side between e.g. Postgres and Neo4J, they will look identical, just that the leaf-nodes in Postgres will be a table-scan, while in Neo4J they will be either a vertex-scan or a edge-scan (which can both be seen as special cases of a table-scan).

As someone that has worked a lot with graph databases in the past, I'd largely recommend not using them. The price you pay in terms of worse ecosystem and less battle-tested maintenance tooling is not worth it to just have better syntax.

I played around with Neo4j and only ever found two domains where it excelled.

- custom app security

- social media

I also think cypher is a brilliant way to query a graph.

I have had very bad experiences with graph dbs at scale. To the point where I will never again work on a project based on a graph db. YMMV, but I'm done with them forever.

[edit] To elaborate: tuning read and write performance is difficult, essentially everything must be indexed. This results in an explosion in data size--so data that might be X TB in csv format becomes maybe 10X when you consider all the indexes and bloat from the graph db's storage format. Which brings us to the topic of compression: there isn't any. Moreover, graph dbs are basically all quite immature compared to other db software, they haven't seen the test of production to the same degree, nor have they gotten the same attention in terms of bugfixes, performance tuning, etc. So they often have very sharp edges that you encounter under load. So if you like spending tons of money and time on zero value work, choose a graph db!

[another edit] Another problem with graph dbs is they encourage laziness in data modelling. I would echo others' recommendations here: just start with SQL. If it gets to the point where you actually need a graph query language--and you've already appropriately considered your life choices and determined that there's some value to what you're doing--only then should you consider trying to implement something on a graph query engine. But I'm not aware of one that is actually good. That's the problem. If you're at the point where you absolutely need a graph database you are probably at the point where you need to build your own. And you'll probably fail like all the other ones.

Graph query languages like Cypher are great, but I am wholly unconvinced by the concept of a dedicated, general-purpose "graph database".

IMO, you're better off just using Postgres/etc, modeling your graph data there, and pulling in subsets of your graph for in-memory analysis. This is for the 99% of enterprises that aren't doing online streaming graph analysis on TB-scale graphs, and the other 1% should probably figure out something tailored* to their specific business model.

* Graph algorithms are more accessible than ever with GenAI code, and efficiently modeling a graph in memory is trivial (it's just structs with pointers to other edges/nodes, plus its nice to have full control over the memory layout).

I worked for the research branch of a children's hospital. At one point, I was brought into vendor evaluations a department wanted to buy. Their tool had something to do with visualizing protein interactions, which was highly networked. It was basically a Neo4j database with a React UI on top of it.