GraphScope
Gephi
Our great sponsors
| GraphScope | Gephi | |
|---|---|---|
| 10 | 48 | |
| 3,101 | 5,669 | |
| 0.8% | 0.7% | |
| 9.7 | 9.6 | |
| about 21 hours ago | 12 days ago | |
| C++ | Java | |
| Apache License 2.0 | GNU General Public License v3.0 only |
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.
GraphScope
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Show HN: Graphlearn-for-PyTorch, distributed graph learning on PyTorch
Optimizing distributed sampling and feature lookup looks really attractive. It's really challenging to deploy GNN training at an industrial-scale for a large graph.
Will GLT be part of graphscope[1] and replacing the current graphscope-for-learning implementation?
- GitHub “allows” unauthorized users “merging” PRs, bypass write permission check
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GraphScope VS CXXGraph - a user suggested alternative
2 projects | 17 Mar 2022
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GraphScope on Colab: Large-Scale Graph Computing from Notebooks to Kubernetes
We are glad to announce the landing of GraphScope on Colab: https://colab.research.google.com/github/alibaba/GraphScope.
GraphScope is a one-stop graph computing systems from Alibaba aimed to address challenges in large-scale graph computation in real production environments. GraphScope releases v0.9, enabling data scientists to develop graph computing workflows for analytical, interactive query and GNN workloads on small graphs in jupyter notebooks in a interactive manner. Once finishing the development and debugging, users can easily deployed their workflows to Kubernetes with one-line change!
To try GraphScope, you could find it on Colab[1], Jupyter Hub[2], or install GraphScope to your environment using pip by:
pip3 install graphscope
For more details of our v0.9 release, please refer to https://github.com/alibaba/GraphScope/releases/tag/v0.9.0
[1]: https://colab.research.google.com/github/alibaba/GraphScope/...
- GraphScope v0.6 Released: Code with Eager, Executive with Lazy
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GraphScope: A One-Stop Large-Scale Graph Computing System
Thanks for you interests on GraphScope!
We do have a concrete plan for k8s-less deployment and we already have an issue [1] to track that. That will be available before the end of March 2021.
To simplify the environment setup process we will release a docker image for end-users, but without docker will be ok as well (requires building from sources).
GraphScope use vineyard [2] as the storage layer for im-memory graph data structures. And current the graph type (aka. ArrowPropertyFragment in GraphScope) uses a set of arrow tables and arrays under the hood.
GraphScope supports a `to_vineyard_dataframe` method on the computation context [3]. We also has a plan for integration between vineyard and dask (may could be delivered in March as well). At that time the interop between dask would be straightforward.
[1]: https://github.com/alibaba/GraphScope/discussions/113
[2]: https://github.com/alibaba/libvineyard
[3]: https://graphscope.io/docs/reference/context.html#graphscope...
GraphScope is a unified distributed graph computing platform that provides a one-stop environment for performing diverse graph operations on a cluster of computers through a user-friendly Python interface. GraphScope makes multi-staged processing of large-scale graph data on compute clusters simple by combining several important pieces of Alibaba technology for analytics, interactive, and graph neural networks (GNN) computation, respectively, and the vineyard store that offers efficient in-memory data transfers.
We just released the version 0.2.0. And along with the release, we launched a public JupyterLab service where you can have a try in your browser: https://try.graphscope.app
Github: https://github.com/alibaba/graphscope. (stars are welcome :)
Gephi
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The Hunt for the Missing Data Type
The following are not exactly what you have asked for.
https://gephi.org/ This implements lots of graph visualization algorithms.
https://strlen.com/treesheets/ Excel for tree data.
- NetworkX – Network Analysis in Python
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🇺🇳 Discover a country UN SDGs concerns w/ Open Metadata on Neo4J
🧑🎨 Enjoy some movie data art experience with Gephi and Runway
- Gephi – The Open Graph Viz Platform
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Engaging with Complex Systems
. Gephi (https://gephi.org)
- [AMA] We're Flat Head Studio, four developers from Austria and we just released We Are One on the Quest - ask us anything!
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piece of software to find /crawl information about yourself?
Maybe try exporting your data from spiderfoot and use a graph tool like Gephi to import your data to and have it generate a graph for you.
- Graph of character interactions in Harry Potter and the Philosopher's Stone
- [AskJS] Looking for a light javascript library to help me build character relationship diagrams
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Countries with the most military conflicts according to Wikipedia (List of wars) [OC]
Tool: custom python scripts and Gephi
What are some alternatives?
janusgraph - JanusGraph: an open-source, distributed graph database
Protégé - Protege Desktop
indradb - A graph database written in rust
Guava - Google core libraries for Java
libvineyard - vineyard (v6d): an in-memory immutable data manager. [Moved to: https://github.com/alibaba/v6d]
JGit - JGit project repository (jgit)
euler - A distributed graph deep learning framework.
Dex - Dex : The Data Explorer -- A data visualization tool written in Java/Groovy/JavaFX capable of powerful ETL and publishing web visualizations.
MPAndroidChart - A powerful 🚀 Android chart view / graph view library, supporting line- bar- pie- radar- bubble- and candlestick charts as well as scaling, panning and animations.
JADE - a pug implementation written in Java (formerly known as jade)
sonic - 🦔 Fast, lightweight & schema-less search backend. An alternative to Elasticsearch that runs on a few MBs of RAM.
Cytoscape.js - Graph theory (network) library for visualisation and analysis