GraphScope

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? [1]: https://github.com/alibaba/GraphScope

We recently noticed some strange email notifications from Github with content "Merged #948 into main.", which told that pull requests on our repo has been merged by stranger who actually doesn't have the write permission to our repo! After further inspection, we found that the merge event is triggered by the creator of the pull request pushing current main commits to the PR's "from" branch. Moreover, when pushing current main to a pull request, - The pull request is displayed as "Merged" - A "PR merged into main" email is sent to all subscribers (mainly the repo owners) - A "PR merged" contribution is displayed on the creator's Github profile Closing dangling pull requests is a quite resonable design, but mark it as "Merged" rather than "Closed" would confuse people to let them think they are hacked at the first glance (note that there ev

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/

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/Gra

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

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