bottleneck
grand-cypher
Our great sponsors
| bottleneck | grand-cypher | |
|---|---|---|
| 2 | 4 | |
| 90 | 61 | |
| - | - | |
| 0.0 | 6.0 | |
| about 2 years ago | 2 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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.
bottleneck
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[D] Dr. Petar Veličković (Deepmind) - Categories, Graphs, Reasoning and Graph Expander Propagation
Found relevant code at https://github.com/tech-srl/bottleneck + all code implementations here
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[N] New Transport Planning Dataset for Deep Graph Neural Networks
Code for https://arxiv.org/abs/2006.05205 found: https://github.com/tech-srl/bottleneck
grand-cypher
What are some alternatives?
GraphMixerNetworks - Official Implementation of Graph Mixer Networks
dotmotif - A performant, powerful query framework to search for network motifs
osmnx - OSMnx is a Python package to easily download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.
grand - Your favorite Python graph libraries, scalable and interoperable. Graph databases in memory, and familiar graph APIs for cloud databases.
code2vec - TensorFlow code for the neural network presented in the paper: "code2vec: Learning Distributed Representations of Code"
movies-python-bolt - Neo4j Movies Example application with Flask backend using the neo4j-python-driver
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.