fixed typos and other gramatical issues

CONTRIBUTING.md

@@ -151,6 +151,7 @@ implementation of the issue, ask them in the issue instead of the PR.
 ### Style Guild
 All Python code most pass flake8 style checking
 All C++ code must pass clang style checking
+All code must adhere to the [RAPIDS Style Guide](https://docs.rapids.ai/resources/style/)
 
 ### Tests
 All code must have associate test cases.  Code without test will not be accepted

README.md

@@ -2,7 +2,7 @@
 
 [![Build Status](https://gpuci.gpuopenanalytics.com/job/rapidsai/job/gpuci/job/cugraph/job/branches/job/cugraph-branch-pipeline/badge/icon)](https://gpuci.gpuopenanalytics.com/job/rapidsai/job/gpuci/job/cugraph/job/branches/job/cugraph-branch-pipeline/)
 
-The [RAPIDS](https://rapids.ai) cuGraph library is a collection of GPU accelerated graph algorithms that process data found in [GPU DataFrames](https://github.com/rapidsai/cudf).  The vision of cuGraph is _to make graph analysis ubiquitous to the point that users just think in terms of analysis and not technologies or frameworks_.  To realize that vision, cuGraph operators, at the Python layer, on GPU DataFrames, 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 scientist familiar with Python will quickly pick up how cuGraph integrates with the Pandas-like API of cuDF.  Likewise, user familiar with NetworkX will quickly reconnize the NetworkX-like API provided in cuGraph, with the goal being to allow existing code to be ported with minimal effort into RAPIDS.  For users familar with C/CUDA and graph structures, a C++ API is also provided.  However, there is less type and structure checking at the C layer.  
+The [RAPIDS](https://rapids.ai) cuGraph library is a collection of GPU accelerated graph algorithms that process data found in [GPU DataFrames](https://github.com/rapidsai/cudf).  The vision of cuGraph is _to make graph analysis ubiquitous to the point that users just think in terms of analysis and not technologies or frameworks_.  To realize that vision, cuGraph operators, at the Python layer, on GPU DataFrames, 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 scientist familiar with Python will quickly pick up how cuGraph integrates with the Pandas-like API of cuDF.  Likewise, user familiar with NetworkX will quickly reconnize the NetworkX-like API provided in cuGraph, with the goal being to allow existing code to be ported with minimal effort into RAPIDS.  For users familiar with C++/CUDA and graph structures, a C++ API is also provided.  However, there is less type and structure checking at the C layer.  
 
  For more project details, see [rapids.ai](https://rapids.ai/).
 

SOURCEBUILD.md

@@ -120,7 +120,7 @@ Note that the libraries will be installed to the location set in `$PREFIX` if se
 
 
 ## Building each section independently
-#### Build and Install the C/CUDA `libcugraph` Library
+#### Build and Install the C++/CUDA `libcugraph` Library
 CMake depends on the `nvcc` executable being on your path or defined in `$CUDACXX`.
 
 This project uses cmake for building the C/C++ library. To configure cmake, run: