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Fix minor typo in README.md #3636

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Jun 5, 2023
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Fix minor typo in README.md #3636

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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -73,7 +73,7 @@

<img src="img/Stack2.png" alt="Stack" width="800">

[RAPIDS](https://rapids.ai) cuGraph is a collection of GPU-accelerated graph algorithms and services. At the Python layer, cuGraph operates on [GPU DataFrames](https://github.com/rapidsai/cudf), thereby allowing for seamless passing of data between ETL tasks in [cuDF](https://github.com/rapidsai/cudf) and machine learning tasks in [cuML](https://github.com/rapidsai/cuml). Data scientists familiar with Python will quickly pick up how cuGraph integrates with the Pandas-like API of cuDF. Likewise, users familiar with NetworkX will quickly recognize the NetworkX-like API provided in cuGraph, with the goal to allow existing code to be ported with minimal effort into RAPIDS. To similfy integration, cuGraph also support data found in [Pandas DataFrame](https://pandas.pydata.org/), [NetworkX Graph Objects](https://networkx.org/) and several other formats.
[RAPIDS](https://rapids.ai) cuGraph is a collection of GPU-accelerated graph algorithms and services. At the Python layer, cuGraph operates on [GPU DataFrames](https://github.com/rapidsai/cudf), thereby allowing for seamless passing of data between ETL tasks in [cuDF](https://github.com/rapidsai/cudf) and machine learning tasks in [cuML](https://github.com/rapidsai/cuml). Data scientists familiar with Python will quickly pick up how cuGraph integrates with the Pandas-like API of cuDF. Likewise, users familiar with NetworkX will quickly recognize the NetworkX-like API provided in cuGraph, with the goal to allow existing code to be ported with minimal effort into RAPIDS. To simplify integration, cuGraph also supports data found in [Pandas DataFrame](https://pandas.pydata.org/), [NetworkX Graph Objects](https://networkx.org/) and several other formats.

While the high-level cugraph python API provides an easy-to-use and familiar interface for data scientists that's consistent with other RAPIDS libraries in their workflow, some use cases require access to lower-level graph theory concepts. For these users, we provide an additional Python API called pylibcugraph, intended for applications that require a tighter integration with cuGraph at the Python layer with fewer dependencies. Users familiar with C/C++/CUDA and graph structures can access libcugraph and libcugraph_c for low level integration outside of python.

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