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chore: README update #850

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# GraphFrames: graph algorithms at scale

This is a package for graphs processing and analytics at scale. It is built on top of Apache Spark and relies on DataFrame abstraction. It provides built-in and easy to use distributed graph algorithms as well as flexible APIs like `Pregel` or `AggregateMessages` to make custom graph processing. Users can write highly expressive queries by leveraging the DataFrame API, combined with a new API for network motif finding. The user also benefits from DataFrame performance optimizations within the Spark SQL engine. GraphFrames works in Java, Scala, and Python.
This is a package for graphs processing and analytics at scale. It is built on top of Apache Spark and relies on DataFrame abstraction. It provides built-in and easy to use distributed graph algorithms as well as flexible APIs like `Pregel`, `AggregateMessages` or `AggregateNeighbors` to make custom graph processing. Users can write highly expressive queries by leveraging the DataFrame API, combined with a new API for network motif finding. The user also benefits from DataFrame performance optimizations within the Spark SQL engine. GraphFrames works in Java, Scala, and Python.

## GraphFrames usecases

There are some popular use cases when GraphFrames is almost irreplaceable, including, but not limited to:

- Compliance analytics with a scalable shortest paths algorithm and motif analysis;
- Anti-fraud with scalable cycles detection in large networks;
- Anti-fraud with scalable cycles detection in large networks and by using K-Core algorithm;
- Identity resolution at the scale of billions with highly efficient connected components;
- Search result ranking with a distributed, Pregel-based PageRank;
- Clustering huge graphs with Label Propagation and Power Iteration Clustering;
- Building a knowledge graph systems with Property Graph Model.
- Plan marketing campaigns in social networks using Maximal Indpendent Set algorithm;
- Rank search result with a distributed, Pregel-based PageRank;
- Cluster huge graphs with Label Propagation and Power Iteration Clustering;
- Compute node embeddings at billion scale using Random-Walks and Hash2Vec model;
- Build a knowledge graph systems with Property Graph Model.

## Documentation

- [Installation](https://graphframes.io/02-quick-start/01-installation.html)
- [Creating Graphs](https://graphframes.io/04-user-guide/01-creating-graphframes.html)
- [Basic Graph Manipulations](https://graphframes.io/04-user-guide/02-basic-operations.html)
- [Centrality Metrics](https://graphframes.io/04-user-guide/03-centralities.html)
- [Motif finding](https://graphframes.io/04-user-guide/04-motif-finding.html)
- [Motif Finding](https://graphframes.io/04-user-guide/04-motif-finding.html)
- [Traversals and Connectivity](https://graphframes.io/04-user-guide/05-traversals.html)
- [Community Detection](https://graphframes.io/04-user-guide/06-graph-clustering.html)
- [Subgraphs](https://graphframes.io/04-user-guide/07-subgraphs.html)
- [Graph Machine Learning](https://graphframes.io/04-user-guide/15-graph-ml.html)
- [Saving and Loading](https://graphframes.io/04-user-guide/08-saving-and-loading.html)
- [Message Passing](https://graphframes.io/04-user-guide/09-aggregate-messages.html)
- [Pregel](https://graphframes.io/04-user-guide/10-pregel.html)
- [Labeled Property Graphs](https://graphframes.io/04-user-guide/11-property-graphs.html)
- [Scala API](https://graphframes.io/api/scaladoc/)
- [Python API](https://graphframes.io/api/python/)
- [Apache Spark compatibility](https://graphframes.io/02-quick-start/01-installation.html#spark-versions-compatibility)
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## GraphFrames Internals

- [A top level overview of GraphFrames internals](https://graphframes.io/01-about/02-architecture.html)
- [GraphFrames: An Integrated API for Mixing Graph and Relational Queries, Dave et al. 2016](https://people.eecs.berkeley.edu/~matei/papers/2016/grades_graphframes.pdf).
- [GraphFrames: An Integrated API for Mixing Graph and Relational Queries, Dave et al. 2016](https://people.eecs.berkeley.edu/~matei/papers/2016/grades_graphframes.pdf)
- [Detailed overview of the GraphFrames Random-Walks and Hash2Vec implementations](https://semyonsinchenko.github.io/ssinchenko/post/graphframes-embeddings/)

## Contributing

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