Graph Engine VS v6d

Compare Graph Engine vs v6d and see what are their differences.

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Graph Engine v6d
0 5
2,171 798
0.7% 1.6%
6.6 9.5
3 months ago 3 days ago
C# C++
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Graph Engine

Posts with mentions or reviews of Graph Engine. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning Graph Engine yet.
Tracking mentions began in Dec 2020.

v6d

Posts with mentions or reviews of v6d. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-25.

What are some alternatives?

When comparing Graph Engine and v6d you can also consider the following projects:

PCLExt.FileStorage - Portable Storage APIs

cpp-ipc - C++ IPC Library: A high-performance inter-process communication using shared memory on Linux/Windows.

shadesmar - Fast C++ IPC using shared memory

zef - Toolkit for graph-relational data across space and time

iceoryx - Eclipse iceoryx™ - true zero-copy inter-process-communication

Erdos - Modular and modern graph-theory algorithms framework in Java

pe-util - List shared object dependencies of a portable executable (PE)

GraphScope - 🔨 🍇 💻 🚀 GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba | 一站式图计算系统

ecal - 📦 eCAL - enhanced Communication Abstraction Layer. A high performance publish-subscribe, client-server cross-plattform middleware.

Memgraph - Open-source graph database, built for real-time streaming data, compatible with Neo4j.

LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.