A Brief Study of Open Source Graph Databases
Rob McColl, David Ediger, Jason Poovey, Dan Campbell, David Bader

TL;DR
This paper compares various open source graph database platforms by implementing real-world algorithms on large synthetic graphs, highlighting their capabilities, interfaces, and performance differences.
Contribution
It provides a comparative analysis of open source graph databases, focusing on their ability to handle large-scale data and support complex graph algorithms.
Findings
STINGER shows high performance on large graphs
Different platforms vary significantly in ease of use and scalability
Some platforms excel in real-time updates and complex analysis
Abstract
With the proliferation of large irregular sparse relational datasets, new storage and analysis platforms have arisen to fill gaps in performance and capability left by conventional approaches built on traditional database technologies and query languages. Many of these platforms apply graph structures and analysis techniques to enable users to ingest, update, query and compute on the topological structure of these relationships represented as set(s) of edges between set(s) of vertices. To store and process Facebook-scale datasets, they must be able to support data sources with billions of edges, update rates of millions of updates per second, and complex analysis kernels. These platforms must provide intuitive interfaces that enable graph experts and novice programmers to write implementations of common graph algorithms. In this paper, we explore a variety of graph analysis and storage…
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Taxonomy
TopicsGraph Theory and Algorithms · Advanced Database Systems and Queries · Data Management and Algorithms
