Hi Sotiris,
Sorry about the belated reply, I read your paper with interest about a
month ago but didn't see this post until now.
Interesting work and results, thanks for sharing (both the links and code)!
I just have one general comment regarding the read workloads and that is
that they appear to be very "global", that is, the operations access most
or all of the graph dataset: Find neighbors OF ALL NODES, Find adjacent
edges OF ALL EDGES, etc.
Graph databases are definitely capable of this type of workload, but it is
not where they truly shine, nor is it what real world usage looks like.
Graph databases are designed with interactive, low latency queries in mind;
similar in nature to how you would use a relational database but far more
capable at querying connected, complex data.

If you're interested, Neo Technology is one of the major contributors in an
EU-funded research project tasked with developing graph database benchmarks
(participating in this is my daily job). For more information about the
project:

   - Site: http://ldbcouncil.org/
   - Github: https://github.com/ldbc
   - Specification for one of the benchmarks we are developing:
   https://github.com/ldbc/ldbc_snb_docs

Feel free to ask any questions.
Best,
Alex

On Wed, Sep 3, 2014 at 12:36 PM, Sotiris Beis <[email protected]> wrote:

> Hi all,
>
> we have released a grapah database benchmark that includes Neo4j. If you
> like take a look here https://github.com/socialsensor/graphdb-benchmarks
> . You can find the presentation here
> http://www.slideshare.net/sympapadopoulos/adbis2014-presentation. Feel
> free to suggest any fix in order to be as objective as possible.
>
> Thanks in advance,
> Sotiris
>
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