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Graph processing has become an important part of multiple areas of computerscience, such as machine learning, computational sciences, medicalapplications, social network analysis, and many others. Numerous graphs such asweb or social networks may contain up to trillions of edges. Often, thesegraphs are also dynamic (their structure changes over time) and havedomain-specific rich data associated with vertices and edges. Graph databasesystems such as Neo4j enable storing, processing, and analyzing such large,evolving, and rich datasets. Due to the sheer size of such datasets, combinedwith the irregular nature of graph processing, these systems face unique designchallenges. To facilitate the understanding of this emerging domain, we presentthe first survey and taxonomy of graph database systems. We focus onidentifying and analyzing fundamental categories of these systems (e.g., triplestores, tuple stores, native graph database systems, or object-orientedsystems), the associated graph models (e.g., RDF or Labeled Property Graph),data organization techniques (e.g., storing graph data in indexing structuresor dividing data into records), and different aspects of data distribution andquery execution (e.g., support for sharding and ACID). 51 graph databasesystems are presented and compared, including Neo4j, OrientDB, or Virtuoso. Weoutline graph database queries and relationships with associated domains (NoSQLstores, graph streaming, and dynamic graph algorithms). Finally, we describeresearch and engineering challenges to outline the future of graph databases.

Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries