jbmusso / awesome-graph

A curated list of resources for graph databases and graph computing tools

awesome-graph

A curated list of resources for graph databases and graph computing tools

Graph databases

• AgensGraph - multi-model graph database with SQL and Cypher support
• AnzoGraph - Massively parallel graph database with advanced analytics (SPARQL, Cypher, OWL/RDFS+, LPG)
• Atomic-Server - open-source type-safe graph database server with GUI, written in rust. Supports Atomic Data, JSON & RDF.
• ArangoDB - highly available Multi-Model NoSQL database
• Blazegraph - GPU accelerated graph database
• Cayley - open source database written in Go
• CosmosDB - cloud-based multi-model database with support for TinkerPop3
• Dgraph - Fast, Transactional, Distributed Graph Database (open source, written in Go)
• DSE Graph - Graph layer on top of DataStax Enterprise (Cassandra, SolR, Spark)
• Grafito - Portable, Serverless & Lightweight SQLite-based Graph Database in Arturo
• Grakn.AI - a distributed hyper-relational database for knowledge-oriented systems, i.e. a distributed knowledge base
• Graphd - the Metaweb/Freebase Graph Repository
• JanusGraph - an open-source, distributed graph database with pluggable storage and indexing backends
• Memgraph - High Performance, In-Memory, Transactional Graph Database
• Neo4j - OLTP graph database
• Nebula Graph - A distributed, fast open-source graph database featuring horizontal scalability and high availability
• RedisGraph - Property graph database, based on linear algebra constructs (GraphBLAS)
• Sparksee - makes space and performance compatible with a small footprint and a fast analysis of large networks
• Stardog - RDF graph database with OLTP and OLAP support
• OrientDB - Distributed Multi-Model NoSQL Database with a Graph Database Engine
• TerminusDB is an open source graph database and document store. It is designed for collaboratively building data-intensive applications and knowledge graphs.
• TigerGraph - The First Native Parallel Graph capable of real-time analytics on web-scale data
• Weaviate - Weaviate is a cloud-native, modular, real-time vector search engine with a graph data model (GraphQL interface) built to scale your machine learning models.

Triple stores

• Akutan - Akutan is a distributed knowledge graph store, sometimes called an RDF store or a triple store
• AllegroGraph - high-performance, persistent graph database that scales to billions of quads
• Apache Jena - open source Java framework for building Semantic Web and Linked Data applications
• Dydra - Dydra is a cloud-based graph database. Dydra stores data is natively stored as a property graph, directly representing the relationships in the underlying data.
• Eclipse RDF4J - (formerly known as Sesame) is an open source Java framework for processing RDF data. This includes parsing, storing, inferencing and querying of/over such data. It offers an easy-to-use API that can be connected to all leading RDF storage solutions. It allows you to connect with SPARQL endpoints and create applications that leverage the power of linked data and Semantic Web.
• GraphDB - enterprise ready Semantic Graph Database, compliant with W3C Standards
• Virtuoso - a "Data Junction Box" that drives enterprise and individual agility by deriving a Semantic Web of Linked Data from existing data silos
• Hoply - explore bigger than RAM relational data in the comfort of Python.

Graph computing frameworks

• Apache Giraph - an iterative graph processing system built for high scalability
• Apache TinkerPop - a graph computing framework for both graph databases (OLTP) and graph analytic systems (OLAP)
• Apache Spark - GraphX - Apache Spark's API for graphs and graph-parallel computation
• GraphScope - A one-stop large-scale graph computing system from Alibaba

Languages

• Cypher
• Datalog
• Gremlin
• SPARQL
• GSQL

Managed hosting services

• CosmosDB @ Microsoft
• JanusGraph @ IBM Compose
• JanusGraph @ Google Cloud Platform - JanusGraph on Google Kubernetes Engine backed by Google Cloud Bigtable
• JanusGraph @ Amazon Web Services Labs
• Neo4j @ Graphene
• Neptune @ Amazon Web Services - a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets

Learning materials

Official documentations

• Cypher - reference documentation
• Gremlin - reference documentation

Community effort

• Graph Book - TinkerPop3 centric book written by Kelvin R. Lawrence
• SQL2Gremlin - transition from SQL to Gremlin by Daniel Kuppitz
• The Gremlin Compendium - minimum survival kit for any Gremlin user, 10 blog post series by Doan DuyHai

Blogs

• TigerGraph Blog

Conferences

• Graph Connect - powered by Neo4j
• Graph Day - an Independent Graph Conference from the Data Day folks

License

To the extent possible under law, Jean-Baptiste Musso has waived all copyright and related or neighboring rights to this work.

awesome-graph's Issues

Adding a new category of resources : Frameworks

Could be interesting not to just mention the stores, but also framweorks for visualization / app development, like (the list below is probably not complete! )

• Graphileon ( that's us)
• Structr
• Linkurious
• Keylines

Should Anzograph make this list

Was browsing and wondered if Anzograph - an OLAP graph database should be on your list?

https://www.cambridgesemantics.com/product/anzograph/

Terminus DB should make it on this list

Hey nice resources collected. Thanks!
I truly believe you should have a look at TerminusDB,

https://github.com/terminusdb/terminusdb

The Neo4J link is broken

It points to http://tinkerpop.apache.org/docs/currentg/#neo4j-gremlin which returns a 404.

Signal Processing on Graphs

Our lab is developing the PyGSP, a python package for graph signal processing. It may be relevant to your graph computing list. Our approach is different from the other packages as we take a signal processing point-of-view to computations. The Emerging Field of Signal Processing on Graphs provides a nice overview.

Add Memgraph Cloud to Managed hosting services section

Memgraph cloud is fully managed on AWS. Can it be added to this list?

Graph DB for 500M vertices and 2Bn edges labelled by 1M indices.

We are looking to use a graph DB. Our data set has about 500M vertices and 2Bn directed edges. Each edge has an index from 0 to 1M (i.e. on average 2000 edges share the same index). We can have parallel edges with different indices. We need to be able to query for a desired index range. Also, each edge has another value attribute, and each vertex has one scalar attribute. We would have to add more vertices and edges as time goes on. We would like to host this on a single server with large memory.

• Which graph databases would scale for this data set?
• How would you model such a data set in a graph DB? Should it be a one giant graph or maybe a series of 1M sub-graphs (e.g. one graph per each index value)?

Please Add `nebula graph database`

Nebula is A new opensource graph database, Our opensource repositry is here: https://github.com/vesoft-inc/nebula.

New section: ML / deep learning

What do you think of adding Graph Convolutional Networks in a new section on ML and deep learning?

Here are some references:

• https://tkipf.github.io/graph-convolutional-networks/
• https://github.com/tkipf/gcn (implementation of the above)
• https://towardsdatascience.com/how-to-do-deep-learning-on-graphs-with-graph-convolutional-networks-7d2250723780

Adding to the list dgraph

Hey, I found this graph database: https://github.com/dgraph-io/dgraph

Add Datalog as a query language

The group of graph databases that use datalog variants as query languages grows everyday - in fact, I would suggest that it is swiftly becoming the dominant query approach in the new generation of graphs dbs. These new superstars all use datalog:

TerminusDB
Grakn
Crux

Dydra to Triplet Stores PR

Created Dydra entry with short explanation.