1. Shortlist (and in no order): Neo4j, AWS Neptune, Datastax Graph, TigerGraph, Azure CosmosDB, and JanusGraph (Titan fork) are the ones we see the most in practice, and not in production but rumor-mill, Dgraph, RedisGraph, & ArangoDB. The three-and-four-letter types seem to roll their own, for better or worse. There are also some super cool ones that don't get visibility outside of the HPC+DoD world, like Stinger & Gunrock. Interestingly, the reality is a ton of our graph users aren't even on graph DBs (think Splunk/ELK/SQL), and for data scientists, just do ephemeral Pandas/Spark. As someone from the early days of the end-to-end GPU computing movement, we're incorporating cuGraph (part of nvidia rapids.ai) into our middle tier, so you get to transparently benefit from it while looking at data in any of the above.
2. I now slice graph DB's more in terms of OLTP (neo4j, janus, neptune, maybe tiger) vs OLAP (spark graphx, cugraph) vs batch (janus, tiger) vs friendly BI/data science (neo4j) vs friendly app dev / multi-modal add-on (CosmosDB, Neo4j, Arango, Redis). Curious to see how this goes -- given the number of contributors, I'm guessing it's doing well in at least one of these. +1 to hearing reports from others!