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README updates (rapidsai#2395)
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updated the README

closes rapidsai#2376

Authors:
  - Brad Rees (https://github.com/BradReesWork)

Approvers:
  - Chuck Hastings (https://github.com/ChuckHastings)
  - Rick Ratzel (https://github.com/rlratzel)

URL: rapidsai#2395
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@BradReesWork
BradReesWork authored Jul 18, 2022
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[![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 operates, at the Python layer, on GPU DataFrames, 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. 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.
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 operates, at the Python layer, on GPU DataFrames, 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.

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.

For more project details, see [rapids.ai](https://rapids.ai/).

Expand All @@ -25,10 +27,9 @@ G.from_cudf_edgelist(gdf, source='src', destination='dst')
# Let's now get the PageRank score of each vertex by calling cugraph.pagerank
df_page = cugraph.pagerank(G)

# Let's look at the PageRank Score (only do this on small graphs)
for i in range(len(df_page)):
print("vertex " + str(df_page['vertex'].iloc[i]) +
" PageRank is " + str(df_page['pagerank'].iloc[i]))
# Let's look at the top 10 PageRank Score
df_page.sort_values('pagerank', ascending=False).head(10)

```

## Getting cuGraph
Expand All @@ -42,7 +43,7 @@ There are 3 ways to get cuGraph :
# cuGraph News

### Scaling to 1 Trillion Edges
cuGraph was recently tested on the Selene supercomputer using 2,048 GPUs and processing a graph with `1.1 Trillion edges`.
At GTC Spring '22 we presented results of running cuGraph on the [Selene](https://top500.org/system/179842/) supercomputer using 2,048 GPUs and processing a graph with `1.1 Trillion edges`. Synthetic data created with the RMAT generator found in cuGraph.

<div align="left"><img src="img/Scaling.png" width="500px" style="background-color: white;"/>&nbsp;</br>cuGraph Scaling</div>
</br></br>
Expand All @@ -53,102 +54,102 @@ cuGraph has a new multi-layer software stack that allows users and system integr
<div align="left"><img src="img/cugraph-stack.png" width="500px" style="background-color: white;"/>&nbsp;</br>cuGraph Software Stack</div>
</br></br>




---
# Currently Supported Features
As of Release 21.08 - including 21.08 nightly
As of Release 22.06

</br></br>
## Supported Data Types
cuGraph supports graph creation with Source and Destination being expressed as:
* cuDF DataFrame
* Pandas DataFrame

## Supported Algorithms
_Italic_ algorithms are planned for future releases.
cuGraph supports execution of graph algorithms from different graph objects
* cuGraph Graph classes
* NetworkX graph classes
* CuPy sparse matrix
* SciPy sparse matrix

| Category | Algorithm | Scale | Notes |
| ------------ | -------------------------------------- | ------------ | ------------------- |
| Centrality | | | |
| | Katz | Multi-GPU | |
| | Betweenness Centrality | Single-GPU | MG planned for 22.08 |
| | Edge Betweenness Centrality | Single-GPU | |
| | _Eigenvector Centrality_ | | _MG planned for 22.06_ |
| Community | | | |
| | Leiden | Single-GPU | |
| | Louvain | Multi-GPU | [C++ README](cpp/src/community/README.md#Louvain) |
| | Ensemble Clustering for Graphs | Single-GPU | |
| | Spectral-Clustering - Balanced Cut | Single-GPU | |
| | Spectral-Clustering - Modularity | Single-GPU | |
| | Subgraph Extraction | Single-GPU | |
| | Triangle Counting | Single-GPU | MG planned for 22.06 |
| | K-Truss | Single-GPU | MG planned for 22.10 |
| Components | | | |
| | Weakly Connected Components | Multi-GPU | |
| | Strongly Connected Components | Single-GPU | MG planned for 22.06 |
| Core | | | |
| | K-Core | Single-GPU | MG planned for 22.10 |
| | Core Number | Single-GPU | MG planned for 22.08 |
| _Flow_ | | | |
| | _MaxFlow_ | --- | |
| _Influence_ | | | |
| | _Influence Maximization_ | --- | |
| Layout | | | |
| | Force Atlas 2 | Single-GPU | |
| Linear Assignment| | | |
| | Hungarian | Single-GPU | [README](cpp/src/linear_assignment/README-hungarian.md) |
| Link Analysis| | | |
| | Pagerank | Multi-GPU | [C++ README](cpp/src/centrality/README.md#Pagerank) |
| | Personal Pagerank | Multi-GPU | [C++ README](cpp/src/centrality/README.md#Personalized-Pagerank) |
| | HITS | Multi-GPU | |
| Link Prediction | | | |
| | Jaccard Similarity | Single-GPU | |
| | Weighted Jaccard Similarity | Single-GPU | |
| | Overlap Similarity | Single-GPU | |
| | Sorensen Coefficient | Single-GPU | |
| | _Local Clustering Coefficient_ | --- | |
| Sampling | | | |
| | Random Walks (RW) | Single-GPU | Biased and Uniform |
| | Egonet | Single-GPU | multi-seed |
| | Node2Vec | Single-GPU | |
| | Neighborhood sampling | Multi-GPU | |
| Traversal | | | |
| | Breadth First Search (BFS) | Multi-GPU | with cutoff support <br/> [C++ README](cpp/src/traversal/README.md#BFS) |
| | Single Source Shortest Path (SSSP) | Multi-GPU | [C++ README](cpp/src/traversal/README.md#SSSP) |
| | _ASSP / APSP_ | | |
| Tree | | | |
| | Minimum Spanning Tree | Single-GPU | |
| | Maximum Spanning Tree | Single-GPU | |
| Other | | | |
| | Renumbering | Multi-GPU | multiple columns, any data type |
| | Symmetrize | Multi-GPU | |
| | Path Extraction | | Extract paths from BFS/SSP results in parallel |
| Data Generator | | | |
| | RMAT | Multi-GPU | |
| | _Barabasi-Albert_ | --- | |
| | |
cuGraph tries to match the return type based on the input type. So a NetworkX input will return the same data type that NetworkX would have.

</br></br>

## Supported Graph
| Type | Description |
| --------------- | --------------------------------------------------- |
| Graph | An undirected Graph is default |
| Graph | An undirected Graph by default |
| | directed=True yields a Directed Graph |
| Multigraph | A Graph with multiple edges between a vertex pair |
| | |

ALL Algorithms support Graphs and MultiGraph (directed and undirected)

## Supported Algorithms
_Italic_ algorithms are planned for future releases.

</br></br>
## Supported Data Types
cuGraph supports graph creation with Source and Destination being expressed as:
* cuDF DataFrame
* Pandas DataFrame
| Category | Algorithm | Scale | Notes |
| ------------ | -------------------------------------- | ------------- | ------------------- |
| Centrality | | | |
| | Katz | <mark>Multi-GPU</mark> | |
| | Betweenness Centrality | Single-GPU | |
| | Edge Betweenness Centrality | Single-GPU | |
| | Eigenvector Centrality | <mark>Multi-GPU</mark> | |
| | Degree Centrality | <mark>Multi-GPU</mark> | Python only |
| Community | | | |
| | Leiden | Single-GPU | |
| | Louvain | <mark>Multi-GPU</mark> | |
| | Ensemble Clustering for Graphs | Single-GPU | |
| | Spectral-Clustering - Balanced Cut | Single-GPU | |
| | Spectral-Clustering - Modularity | Single-GPU | |
| | Subgraph Extraction | Single-GPU | |
| | Triangle Counting | <mark>Multi-GPU</mark> | |
| | K-Truss | Single-GPU | |
| Components | | | |
| | Weakly Connected Components |<mark>Multi-GPU</mark> | |
| | Strongly Connected Components | Single-GPU | |
| Core | | | |
| | K-Core | Single-GPU | |
| | Core Number | Single-GPU | |
| _Flow_ | | | |
| | _MaxFlow_ | --- | |
| _Influence_ | | | |
| | _Influence Maximization_ | --- | |
| Layout | | | |
| | Force Atlas 2 | Single-GPU | |
| Linear Assignment| | | |
| | Hungarian | Single-GPU | [README](cpp/src/linear_assignment/README-hungarian.md) |
| Link Analysis| | | |
| | Pagerank | <mark>Multi-GPU</mark> | [C++ README](cpp/src/centrality/README.md#Pagerank) |
| | Personal Pagerank | <mark>Multi-GPU</mark> | [C++ README](cpp/src/centrality/README.md#Personalized-Pagerank) |
| | HITS | <mark>Multi-GPU</mark> | |
| Link Prediction | | | |
| | Jaccard Similarity | Single-GPU | |
| | Weighted Jaccard Similarity | Single-GPU | |
| | Overlap Similarity | Single-GPU | |
| | Sorensen Coefficient | Single-GPU | Python only |
| | _Local Clustering Coefficient_ | --- | |
| Sampling | | | |
| | Random Walks (RW) | Single-GPU | Biased and Uniform |
| | Egonet | Single-GPU | multi-seed |
| | Node2Vec | Single-GPU | |
| | Neighborhood sampling | <mark>Multi-GPU</mark> | |
| Traversal | | | |
| | Breadth First Search (BFS) | <mark>Multi-GPU</mark> | with cutoff support <br/> [C++ README](cpp/src/traversal/README.md#BFS) |
| | Single Source Shortest Path (SSSP) | <mark>Multi-GPU</mark> | [C++ README](cpp/src/traversal/README.md#SSSP) |
| | _ASSP / APSP_ | | |
| Tree | | | |
| | Minimum Spanning Tree | Single-GPU | |
| | Maximum Spanning Tree | Single-GPU | |
| Other | | | |
| | Renumbering | <mark>Multi-GPU</mark> | multiple columns, any data type |
| | Symmetrize | <mark>Multi-GPU</mark> | |
| | Path Extraction | | Extract paths from BFS/SSP results in parallel |
| Data Generator | | | |
| | RMAT | <mark>Multi-GPU</mark> | |
| | _Barabasi-Albert_ | --- | |
| | |

cuGraph supports execution of graph algorithms from different graph objects
* cuGraph Graph classes
* NetworkX graph classes
* CuPy sparse matrix
* SciPy sparse matrix

cuGraph tries to match the return type based on the input type. So a NetworkX input will return the same data type that NetworkX would have.


## cuGraph Notice
Expand Down Expand Up @@ -210,6 +211,15 @@ Please see our [guide for contributing to cuGraph](CONTRIBUTING.md).
## Documentation
Python API documentation can be generated from [docs](docs) directory.

------
# Projects that use cuGraph

(alphabetical order)
* ArangoDB - a free and open-source native multi-model database system - https://www.arangodb.com/
* CuPy - "NumPy/SciPy-compatible Array Library for GPU-accelerated Computing with Python" - https://cupy.dev/
* Memgraph - In-memory database - https://memgraph.com/
* ScanPy - a scalable toolkit for analyzing single-cell gene expression data - https://scanpy.readthedocs.io/en/stable/



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