I did a lot of research on graph database technologies recently and read a lot of these "let's compare X to Y" articles. What I found is that most benchmarks - especially those done by people affiliated with a given product - often tend to show a distorted and sometimes plain wrong picture.

For example, concerning the performance and scalability of graph databases the main argument of proponents of this technology is the "join bomb" argument, which states that you can't efficiently store a graph in a relational database since it will require O(log(n)) time to lookup neighboring nodes from the index when crawling the graph. However, this is of course only true for B-tree indexes, whereas hash-based indexing would give you basically the same performance (O(1)) on a graph implemented in a relational database.

Additional features like documents and deep indexes are nice of course but can be (and often are) implemented using relational databases as well, so in the end there really isn't such a large advantage to be gained from using a graph database, especially when taking into account the immaturity of many solutions in that space.