graphs-cpp VS QuickQanava

Compare graphs-cpp vs QuickQanava and see what are their differences.

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graphs-cpp QuickQanava
1 4
16 1,094
- -
1.8 8.8
over 3 years ago about 5 hours ago
C++ C++
BSD 3-clause "New" or "Revised" License GNU General Public License v3.0 or later
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.

graphs-cpp

Posts with mentions or reviews of graphs-cpp. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-08-23.

QuickQanava

Posts with mentions or reviews of QuickQanava. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-04.

What are some alternatives?

When comparing graphs-cpp and QuickQanava you can also consider the following projects:

parallel-dfs-dag - A parallel implementation of DFS for Directed Acyclic Graphs (https://research.nvidia.com/publication/parallel-depth-first-search-directed-acyclic-graphs)

nodeeditor - Qt Node Editor. Dataflow programming framework

CGal - The public CGAL repository, see the README below

libgrape-lite - 🍇 A C++ library for parallel graph processing (GRAPE) 🍇

FunctionalPlus - Functional Programming Library for C++. Write concise and readable C++ code.

vg - tools for working with genome variation graphs

varia

fdg - A Force Directed Graph Drawing Library

Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

netsci-labs - (In progress) Network science laboratories. Covers graph theory, random graphs and ML on graphs

Django - The Web framework for perfectionists with deadlines.

NetworkX - Network Analysis in Python