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ArangoDB database is an open-source NoSQL solution with a flexible data model for documents, graphs, and key-values.

The Document-Graph database, SQL and Native Queries, asynchronous commands, intents, and much more.

Titan is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. Titan is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time.

Wikibase is the software that runs Wikidata, but is also usable for other projects beyond that. Wikibase allows the collaborative editing of structured data on top of MediaWiki. It is optimized for graph-like, schema-poor data, not for tabular and strongly pre-structured data.

FlockDB is a distributed graph database for storing adjancency lists, with goals of supporting:

Unlike traditional relational databases or Property Graph Databases, AllegroGraph employs semantic graph technologies that process data with contextual and conceptual intelligence. AllegroGraph is able run queries of unprecedented complexity to support predictive analytics that help organizations make more informed, real-time decisions. AllegroGraph is the first Graph Database to support analysis across N-dimensions - any conceivable measurement of an object, property or operation. For example, AllegroGraph can analyze temporal (time) and geospatial (location) dimensions relative to any 'event,' such as a disease, drug interaction, genetic combination, biomarkers, observations, image or physical sensors. »

Bitsy is a small, fast, embeddable, durable in-memory graph database that implements the Blueprints API. It supports ACID transactions with optimistic concurrency control and on-disk persistence.

NetworkX is a Python language software package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library.

Graph Engine is a distributed, in-memory, large graph processing engine.