GraphScope
libgrape-lite
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GraphScope | libgrape-lite | |
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10 | 3 | |
3,090 | 363 | |
1.7% | 1.7% | |
9.7 | 6.3 | |
2 days ago | about 21 hours ago | |
C++ | C++ | |
Apache License 2.0 | Apache License 2.0 |
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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 โallowsโ unauthorized users โmergingโ 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: A One-Stop Large-Scale Graph Computing System
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 :)
Website: https://graphscope.io
Documentation: https://graphscope.io/docs
Any comments and contributions from the community are welcomed!
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 :)
libgrape-lite
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libgrape-lite VS CXXGraph - a user suggested alternative
2 projects | 17 Mar 2022
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GraphScope: A One-Stop Large-Scale Graph Computing System
We don't have a benchmark between the analytical engine in GraphScope (aka. GAE) with GraphX/Giraph. But we do have evaluated the performance of the underlying engine of GAE (libgrape-lite) with LDBC Graph Analytics Benchmark and it achieves higher performance comparably to the state-of-the-art systems [2].
[1]: https://github.com/alibaba/libgrape-lite
[2]: https://github.com/alibaba/libgrape-lite/blob/master/Perform...
Yes, we intend to cover the functionality provided by GraphLab, but with better performance (see https://github.com/alibaba/libgrape-lite/blob/master/Perform..., We are actually 10x~50x faster...).
Also we also provide the ability to do Gremlin queries on graphs as well as GNN with TensorFlow, neither is provided with GraphLab
What are some alternatives?
indradb - A graph database written in rust
janusgraph - JanusGraph: an open-source, distributed graph database
QuickQanava - :link: C++17 network / graph visualization library - Qt6 / QML node editor.
libvineyard - vineyard (v6d): an in-memory immutable data manager. [Moved to: https://github.com/alibaba/v6d]
euler - A distributed graph deep learning framework.
MPAndroidChart - A powerful ๐ Android chart view / graph view library, supporting line- bar- pie- radar- bubble- and candlestick charts as well as scaling, panning and animations.
sonic - ๐ฆ Fast, lightweight & schema-less search backend. An alternative to Elasticsearch that runs on a few MBs of RAM.
parallel-disk-usage - Highly parallelized, blazing fast directory tree analyzer
CXXGraph - Header-Only C++ Library for Graph Representation and Algorithms
HPCInfo - Information about many aspects of high-performance computing. Wiki content moved to ~/docs.