Data Modelling Topologies of a Graph Database

Definition and Classification of Graph Databases into Three Categories

6 minute read


There is a lot of confusion with the definition of graph databases. In my opinion, any definition that avoids any reference to the semantics of nodes and edges or their internal structure is preferable. Failing to follow this guideline, it is unavoidable to favor specific implementations, e.g. Property Graph Databases or Triple Stores, and you may easily become myopic to other types that are based on different models, e.g. hypergraph databases, or different data storage paradigms, e.g. key-value stores. Therefore, I propose we adopt a vendor neutral definition, such as the following one, which cannot exclude any future type of graph database.

A Graph Database is a database that uses a graph topology, i.e. vertices and edges, to manage information at the conceptual level independent of the logical and physical implementation of the graph data structure.

Athanassios I. Hatzis

Graph Databases Per Data Model

That said, there are many differences regarding to the abstraction layer of databases. These affect everything — visualization, query language, indexing, scaling, and transactions. Now, let me focus on the conceptual/logical layer, where my work is based. Depending on the structure of nodes and edges, one can describe the following three different data models.

  1. Property Graph Data Model
    • Directed Labeled Graph
    • Entity-centric with embedded properties and edges with bidirectional linking to nodes
    • Neo4J, OrientDB, ArrangoDB, etc.
  2. Triple/Quadruple Data Model
    • Directed Labeled Graph
    • Edge-centric with unidirectional linking on vertices
    • GraphDB, AllegroGraph, OpenLink Virtuoso, etc.
  3. Associative Data Model
    • Hypergraph/Bipartite Graph
    • Hypernodes, Hyperedges with bidirectional linking
    • Topic Map Data Model, R3DM/S3DM, X10SYS (AtomicDB), HypergraphDB, Qlik Technology

There are two main differences between (1) and (2). First, the type of edges in a property graph, by definition, is bidirectional. You can traverse any edge both ways, despite the fact there is a direction on the edge. On the contrary, with RDF, you have to define two labeled edges with opposite directions to achieve bidirectional linking. And secondly, in literal triples, object parts are properties of a subject part, but they are not first-class citizens and they are not embedded inside the structure of Entity nodes of a property graph.

I left the associative data model as the last thing to mention. R3DM/S3DM is the reincarnation of Topic Maps, the de facto standard for the representation of associations. The following series of posts on associative data modeling is written with a hands-on practice style. It is an attempt to clear the information glut of many-to-many relationships (a.k.a associations) with a thorough examination of well-known data models and at the same time introduce R3DM/S3DM to the public.

  • Part 1 - Relation, Relationship, and Association
  • Part 2 - Association in Topic Map Data Model
  • Part 3 - Association in Property Graph Data Model
  • Part 4 - Association in RDF Data Model
  • Part 5 - Qlik Associative Mode
  • Part 6 - R3DM/S3DM Associative Semiotic Hypergraph Data Model


The verdict from this quick review on graph databases is that I have reasons to believe that associative data modeling is far more powerful and expressive than the other two. I foresee that DBMS vendors that will incorporate in their products R3DM/S3DM technology will eventually have a significant competitive advantage.


R3DM Project Posts


The wizards of stored computer program and the next generation of programmers
The fundamental aspect that software pioneers have been missing when they invented new programming languages or new nosql databases
Have you noticed that what ever the model and data structure in databases we cannot escape from the fundamental principle of managing data allocation space with references, i.e. pointer based logic, memory addressing ?

Associative Semiotic Hypergraph API in Mathematica for Next-Generation BI Systems
European Wolfram Technology Conference 19-20 June 2017 in Amsterdam
My speech at European Wolfram Technology Conference 2017 about a new data modeling framework R3DM/S3DM that is implemented on top of OrientDB graph database and coded in Wolfram Mathematica

Are our old data model standards out of shape ?
An overview of critical points to consider when modeling with R3DM/S3DM
Both Topic Maps and RDF/OWL exhibit signs of aging. These signs do not indicate maturity levels but on the contrary they signal a re-examination of the data modeling, information representation problem

The three dimensions of AI and a fourth one as the key to unlock them
Comments on a review of AI by John Launchbury, special assistant to DIRO, DARPA
Although there has been significant progress with first and second generation AI systems in reasoning, learning and perceiving, abstraction has not been part of the game. The mechanism of abstraction can unify these other three processes.

Associative Data Modelling Demystified: Part 6/6
R3DM/S3DM: Build Powerful, Meaningful, Cohesive Relationships Easily
Demonstration of a new data model framework that transforms OrientDB into a HyperGraph Database

Data Modelling Topologies of a Graph Database
Definition and Classification of Graph Databases into Three Categories
The associative data graph database model is still a heavy hitter, stacking up well against property graphs and triples/quadruples. Expect a comeback.

A Quick Guide on How to Prevail in the Graph Database Arena
A brief discussion on criteria to meet a differentiation strategy for graph databases
A swift introduction to the key factors that influence the performance and unification character of graph databases

Associative Data Modeling Demystified: Part 5/6
Qlik Associative Model
Qlik's competitive advantage over other BI tools is that it manages associations in memory at the engine level and not at the application level. Every data point in every field of a table is associated with every other data point anywhere in the entire schema.


Associative Data Modeling Demystified: Part 4/6
Association in RDF Data Model
In this article we will see how we can define an association in RDF and what are the differences with other data models that we analyzed in previous posts of our series

Do you Understand Many-to-Many Relationships ?
Associative entities are represented differently in various data models
It is 2016 and in my opinion the situation with associative entities has become darn confusing. Edges of a Property Graph data model are bidirectional but RDF links are unidirectional.

Associative Data Modeling Demystified: Part 3/6
Association in Property Graph Data Model
In this article, we continue our investigation with the Property Graph Data model. We discuss how a many-to-many relationship is represented and compare its structure in other data models

Associative Data Modeling Demystified: Part 2/6
Association in Topic Map Data Model
In this post, we demonstrate how Topic Map data model represents associations. In order to link the two, we continue with another SQL query from our relational database

Associative Data Modeling Demystified: Part 1/6
Relation, Relationship and Association
In this article, we introduce the concept of association from the perspective of Entity-Relationship (ER) data model and illustrate it with the modeling of a toy dataset


Towards a New Data Modelling Architecture
Part 2: Atomic Information Resource (AIR)
We introduce the Atomic Information Resource (AIR) unit of R3DM conceptual framework

Towards a New Data Modelling Architecture
Part1 - Relational/ER Constructs in Wolfram Language
We start with terms and constructs that most of us are familiar with from the Relational and Entity-Relationship database management systems