Update README.md (#34)

* Update README.md

- Add conda install instructions
- Show basic usage of calling algorithms
- Show usage as a NetworkX plugin

Author: jim22k

README.md

@@ -9,11 +9,154 @@
 [![Discord](https://img.shields.io/badge/Chat-Discord-blue)](https://discord.com/invite/vur45CbwMz)
 <!--- [![Docs](https://readthedocs.org/projects/graphblas-algorithms/badge/?version=latest)](https://graphblas-algorithms.readthedocs.io/en/latest/) --->
 
-GraphBLAS algorithms written in Python with [Python-graphblas](https://github.com/python-graphblas/python-graphblas).  We are trying to target the NetworkX API algorithms where possible.
+GraphBLAS algorithms written in Python with [Python-graphblas](https://python-graphblas.readthedocs.io/en/latest/).  We are trying to target the NetworkX API algorithms where possible.
 
 ### Installation
 ```
+conda install -c conda-forge graphblas-algorithms
+```
+```
 pip install graphblas-algorithms
 ```
 
-This is a work in progress.  Stay tuned (or come help 😃)!
+## Basic Usage
+
+First, create a GraphBLAS Matrix.
+
+```python
+import graphblas as gb
+
+M = gb.Matrix.from_coo(
+  [0, 0, 1, 2, 2, 3],
+  [1, 3, 0, 0, 1, 2],
+  [1., 2., 3., 4., 5., 6.],
+  nrows=4, ncols=4, dtype='float32'
+)
+```
+
+Next wrap the Matrix as `ga.Graph`.
+
+```python
+import graphblas_algorithms as ga
+
+G = ga.Graph(M)
+```
+
+Finally call an algorithm.
+
+```python
+hubs, authorities = ga.hits(G)
+```
+
+When the result is a value per node, a `gb.Vector` will be returned.
+In the case of [HITS](https://en.wikipedia.org/wiki/HITS_algorithm),
+two Vectors are returned representing the hubs and authorities values.
+
+Algorithms whose result is a subgraph will return `ga.Graph`.
+
+## Plugin for NetworkX
+
+Dispatching to plugins is a new feature in Networkx 3.0.
+When both `networkx` and `graphblas-algorithms` are installed in an
+environment, calls to NetworkX algorithms can be dispatched to the
+equivalent version in `graphblas-algorithms`.
+
+### Dispatch Example
+```python
+import networkx as nx
+import graphblas_algorithms as ga
+
+# Generate a random graph (5000 nodes, 1_000_000 edges)
+G = nx.erdos_renyi_graph(5000, 0.08)
+
+# Explicitly convert to ga.Graph
+G2 = ga.Graph.from_networkx(G)
+
+# Pass G2 to NetworkX's k_truss
+T5 = nx.k_truss(G2, 5)
+```
+
+`G2` is not a `nx.Graph`, but it does have an attribute
+`__networkx_plugin__ = "graphblas"`. This tells NetworkX to
+dispatch the k_truss call to graphblas-algorithms. This link
+connection exists because graphblas-algorithms registers
+itself as a "networkx.plugin" entry point.
+
+The result `T5` is a `ga.Graph` representing the 5-truss structure of the
+original graph. To convert to a NetworkX Graph, use:
+```python
+T5.to_networkx()
+```
+
+Note that even with the conversions to and from `ga.Graph`, this example still runs 10x
+faster than using the native NetworkX k-truss implementation. Speed improvements scale
+with graph size, so larger graphs will see an even larger speed-up relative to NetworkX.
+
+### Plugin Algorithms
+
+The following NetworkX algorithms have been implemented
+by graphblas-algorithms and can be used following the
+dispatch pattern shown above.
+
+- Boundary
+  - edge_boundary
+  - node_boundary
+- Centrality
+  - degree_centrality
+  - eigenvector_centrality
+  - in_degree_centrality
+  - katz_centrality
+  - out_degree_centrality
+- Cluster
+  - average_clustering
+  - clustering
+  - generalized_degree
+  - square_clustering
+  - transitivity
+  - triangles
+- Community
+  - inter_community_edges
+  - intra_community_edges
+- Core
+  - k_truss
+- Cuts
+  - boundary_expansion
+  - conductance
+  - cut_size
+  - edge_expansion
+  - mixing_expansion
+  - node_expansion
+  - normalized_cut_size
+  - volume
+- DAG
+  - ancestors
+  - descendants
+- Dominating
+  - is_dominating_set
+- Isolate
+  - is_isolate
+  - isolates
+  - number_of_isolates
+- Link Analysis
+  - hits
+  - pagerank
+- Reciprocity
+  - overall_reciprocity
+  - reciprocity
+- Regular
+  - is_k_regular
+  - is_regular
+- Shortest Paths
+  - has_path
+- Simple Paths
+  - is_simple_path
+- S Metric
+  - s_metric
+- Structural Holes
+  - mutual_weight
+- Tournament
+  - is_tournament
+  - score_sequence
+  - tournament_matrix
+- Triads
+  - is_triad

graphblas_algorithms/interface.py

@@ -1,5 +1,9 @@
 from . import nxapi
 
+#######
+# NOTE: Remember to update README.md when adding or removing algorithms from Dispatcher
+#######
+
 
 class Dispatcher:
     # Boundary