Kotori VS NetworkX

Compare Kotori vs NetworkX and see what are their differences.

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Kotori NetworkX
- 61
106 14,398
-0.9% 1.5%
4.9 9.5
11 days ago 5 days ago
Python Python
GNU Affero General Public License v3.0 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.
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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.

Kotori

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

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

NetworkX

Posts with mentions or reviews of NetworkX. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-04.
  • Routes to LANL from 186 sites on the Internet
    1 project | news.ycombinator.com | 4 Mar 2024
    Built from this data... https://github.com/networkx/networkx/blob/main/examples/grap...
  • The Hunt for the Missing Data Type
    10 projects | news.ycombinator.com | 4 Mar 2024
    I think one of the elements that author is missing here is that graphs are sparse matrices, and thus can be expressed with Linear Algebra. They mention adjacency matrices, but not sparse adjacency matrices, or incidence matrices (which can express muti and hypergraphs).

    Linear Algebra is how almost all academic graph theory is expressed, and large chunks of machine learning and AI research are expressed in this language as well. There was recent thread here about PageRank and how it's really an eigenvector problem over a matrix, and the reality is, all graphs are matrices, they're typically sparse ones.

    One question you might ask is, why would I do this? Why not just write my graph algorithms as a function that traverses nodes and edges? And one of the big answers is, parallelism. How are you going to do it? Fork a thread at each edge? Use a thread pool? What if you want to do it on CUDA too? Now you have many problems. How do you know how to efficiently schedule work? By treating graph traversal as a matrix multiplication, you just say Ax = b, and let the library figure it out on the specific hardware you want to target.

    Here for example is a recent question on the NetworkX repo for how to find the boundary of a triangular mesh, it's one single line of GraphBLAS if you consider the graph as a matrix:

    https://github.com/networkx/networkx/discussions/7326

    This brings a very powerful language to the table, Linear Algebra. A language spoken by every scientist, engineer, mathematician and researcher on the planet. By treating graphs like matrices graph algorithms become expressible as mathematical formulas. For example, neural networks are graphs of adjacent layers, and the operation used to traverse from layer to layer is matrix multiplication. This generalizes to all matrices.

    There is a lot of very new and powerful research and development going on around sparse graphs with linear algebra in the GraphBLAS API standard, and it's best reference implementation, SuiteSparse:GraphBLAS:

    https://github.com/DrTimothyAldenDavis/GraphBLAS

    SuiteSparse provides a highly optimized, parallel and CPU/GPU supported sparse Matrix Multiplication. This is relevant because traversing graph edges IS matrix multiplication when you realize that graphs are matrices.

    Recently NetworkX has grown the ability to have different "graph engine" backends, and one of the first to be developed uses the python-graphblas library that binds to SuiteSparse. I'm not a directly contributor to that particular work but as I understand it there has been great results.

  • Build the dependency graph of your BigQuery pipelines at no cost: a Python implementation
    2 projects | dev.to | 11 Jan 2024
    In the project we used Python lib networkx and a DiGraph object (Direct Graph). To detect a table reference in a Query, we use sqlglot, a SQL parser (among other things) that works well with Bigquery.
  • NetworkX – Network Analysis in Python
    1 project | /r/patient_hackernews | 9 Dec 2023
    1 project | /r/hackernews | 9 Dec 2023
    1 project | /r/hypeurls | 8 Dec 2023
    8 projects | news.ycombinator.com | 8 Dec 2023
  • Custom libraries and utility tools for challenges
    1 project | /r/adventofcode | 5 Dec 2023
    If you program in Python, can use NetworkX for that. But it's probably a good idea to implement the basic algorithms yourself at least one time.
  • Google open-sources their graph mining library
    7 projects | news.ycombinator.com | 3 Oct 2023
    For those wanting to play with graphs and ML I was browsing the arangodb docs recently and I saw that it includes integrations to various graph libraries and machine learning frameworks [1]. I also saw a few jupyter notebooks dealing with machine learning from graphs [2].

    Integrations include:

    * NetworkX -- https://networkx.org/

    * DeepGraphLibrary -- https://www.dgl.ai/

    * cuGraph (Rapids.ai Graph) -- https://docs.rapids.ai/api/cugraph/stable/

    * PyG (PyTorch Geometric) -- https://pytorch-geometric.readthedocs.io/en/latest/

    --

    1: https://docs.arangodb.com/3.11/data-science/adapters/

    2: https://github.com/arangodb/interactive_tutorials#machine-le...

  • org-roam-pygraph: Build a graph of your org-roam collection for use in Python
    2 projects | /r/orgmode | 7 May 2023
    org-roam-ui is a great interactive visualization tool, but its main use is visualization. The hope of this library is that it could be part of a larger graph analysis pipeline. The demo provides an example graph visualization, but what you choose to do with the resulting graph certainly isn't limited to that. See for example networkx.

What are some alternatives?

When comparing Kotori and NetworkX you can also consider the following projects:

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

Numba - NumPy aware dynamic Python compiler using LLVM

SymPy - A computer algebra system written in pure Python

Dask - Parallel computing with task scheduling

MerkavaDB - A fast ordered NoSQL database.

julia - The Julia Programming Language

RDKit - The official sources for the RDKit library

NumPy - The fundamental package for scientific computing with Python.

snap - Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.

SciPy - SciPy library main repository

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