grand-cypher VS bottleneck

Compare grand-cypher vs bottleneck and see what are their differences.

grand-cypher

Implementation of the Cypher language for searching NetworkX graphs (by aplbrain)

bottleneck

Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications" (by tech-srl)
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grand-cypher bottleneck
4 2
65 90
- -
6.1 0.0
5 days ago about 2 years ago
Python Python
Apache License 2.0 MIT License
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.

grand-cypher

Posts with mentions or reviews of grand-cypher. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-10-30.

bottleneck

Posts with mentions or reviews of bottleneck. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-09.

What are some alternatives?

When comparing grand-cypher and bottleneck you can also consider the following projects:

dotmotif - A performant, powerful query framework to search for network motifs

GraphMixerNetworks - Official Implementation of Graph Mixer Networks

grand - Your favorite Python graph libraries, scalable and interoperable. Graph databases in memory, and familiar graph APIs for cloud databases.

osmnx - OSMnx is a Python package to easily download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.

movies-python-bolt - Neo4j Movies Example application with Flask backend using the neo4j-python-driver

code2vec - TensorFlow code for the neural network presented in the paper: "code2vec: Learning Distributed Representations of Code"

GAT - Graph Attention Networks (https://arxiv.org/abs/1710.10903)

how_attentive_are_gats - Code for the paper "How Attentive are Graph Attention Networks?" (ICLR'2022)

TransportPlanningDataset - A graph based strategic transport planning dataset, aimed at creating the next generation of deep graph neural networks for transfer learning. Based on simulation results of the Four Step Model in PTV Visum.