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.

— ** Athanassios I. Hatzis, 28th February 2017**

## Graph Databases Per Data Model

**Triple/Quadruple Data Model**

**Associative Data Model**

## Epilogue

That said, there are many differences in the conceptual, logical, and physical layers of graph 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.

**Property Graph Data Model**

Entity-centric with embedded properties and edges with

**bidirectional linking**— Directed Labeled GraphsNeo4J, OrientDB, ArrangoDB, etc.

- Edge-centric with
**unidirectional linking**on vertices — Directed Labeled Graphs - GraphDB, AllegroGraph, OpenLink Virtuoso, etc.

- Hypernodes, Hyperedges,
**bidirectional linking —**Hypergraph/Bipartite Graph - 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.

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.

© 2020 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Statistics -- New Foundations, Toolbox, and Machine Learning Recipes
- Book: Classification and Regression In a Weekend - With Python
- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central