[DOC]: Fix invalid links and add materials to notebooks

docs/cugraph/source/graph_support/algorithms/Centrality.md

@@ -15,13 +15,15 @@ But which vertices are most important? The answer depends on which measure/algor
 
 |Algorithm          |Notebooks Containing                                                     |Description                                                  |
 | --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
-|[Degree Centrality](./degree_centrality.html)| [Centrality](./Centrality.ipynb), [Degree](./Degree.ipynb)                   |Measure based on counting direct connections for each vertex|
-|[Betweenness Centrality](./betweenness_centrality.html)| [Centrality](./Centrality.ipynb), [Betweenness](./Betweenness.ipynb)                    |Number of shortest paths through the vertex|
-|[Eigenvector Centrality](./eigenvector_centrality.html)|[Centrality](./Centrality.ipynb), [Eigenvector](./Eigenvector.ipynb)|Measure of connectivity to other important vertices (which also have high connectivity) often referred to as the influence measure of a vertex|
-|[Katz Centrality](./katz_centrality.html)|[Centrality](./Centrality.ipynb), [Katz](./Katz.ipynb)                                         |Similar to Eigenvector but has tweaks to measure more weakly connected graph  |
-|Pagerank|[Centrality](./Centrality.ipynb), [Pagerank](../../link_analysis/Pagerank.ipynb)                                         |Classified as both a link analysis and centrality measure by quantifying incoming links from central vertices.  |
+|[Degree Centrality](./degree_centrality.md)| [Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb), [Degree](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Degree.ipynb)                   |Measure based on counting direct connections for each vertex|
+|[Betweenness Centrality](./betweenness_centrality.md)| [Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb), [Betweenness](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Betweenness.ipynb)                    |Number of shortest paths through the vertex|
+|[Eigenvector Centrality](./eigenvector_centrality.md)|[Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb), [Eigenvector](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Eigenvector.ipynb)|Measure of connectivity to other important vertices (which also have high connectivity) often referred to as the influence measure of a vertex|
+|[Katz Centrality](./katz_centrality.md)|[Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb), [Katz](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Katz.ipynb)                                         |Similar to Eigenvector but has tweaks to measure more weakly connected graph  |
+|Pagerank|[Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb), [Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb)                                         |Classified as both a link analysis and centrality measure by quantifying incoming links from central vertices.  |
+
+[System Requirements](https://github.com/rapidsai/cugraph/blob/main/notebooks/README.md#requirements)
+
 
-[System Requirements](../../README.html#requirements)
 
 | Author Credit |    Date    |  Update          | cuGraph Version |  Test Hardware |
 | --------------|------------|------------------|-----------------|----------------|

docs/cugraph/source/installation/getting_cugraph.md

@@ -9,7 +9,7 @@ There are 4 ways to get cuGraph packages:
 1. [Quick start with Docker Repo](#docker)
 2. [Conda Installation](#conda)
 3. [Pip Installation](#pip)
-4. [Build from Source](#SOURCE)
+4. [Build from Source](./source_build.md)
 
 
 <br>

notebooks/algorithms/centrality/Centrality.ipynb

@@ -36,22 +36,34 @@
     "__Degree Centrality__ <br>\n",
     "Degree centrality is based on the notion that whoever has the most connections must be important.   \n",
     "\n",
-    "$C_d(v) = \\frac{{\\text{{degree of vertex }} v}}{{\\text{{total number of vertices}} - 1}}$\n",
-    "\n",
+    "$C_{degree}(v) = \\frac{{\\text{degree of vertex} \\ v}}{{\\text{total number of vertices} - 1}}$\n",
     "\n",
+    "See:\n",
+    "* [Degree (graph theory) on Wikipedia](https://en.wikipedia.org/wiki/Degree_(graph_theory)) for more details on the algorithm.\n",
+    "* [Learn more about Degree Centrality](https://www.sci.unich.it/~francesc/teaching/network/degree.html)\n",
     "\n",
-    "___Closeness centrality – coming soon___ <br>\n",
+    "__Closeness Centrality__ <br>\n",
     "Closeness is a measure of the shortest path to every other node in the graph.  A node that is close to every other node, can reach over other node in the fewest number of hops, means that it has greater influence on the network versus a node that is not close.\n",
     "\n",
+    "$C_{closeness}(v)=\\frac{n-1}{\\sum_{t} d(v,t)}$\n",
+    "\n",
+    "See:\n",
+    "* [Closeness Centrality on Wikipedia](https://en.wikipedia.org/wiki/Closeness_centrality) for more details on the algorithm.\n",
+    "* [Learn more about Closeness Centrality](https://www.sci.unich.it/~francesc/teaching/network/closeness.html)\n",
+    "\n",
     "__Betweenness Centrality__ <br>\n",
     "Betweenness is a measure of the number of shortest paths that cross through a node, or over an edge.  A node with high betweenness means that it had a greater influence on the flow of information.  \n",
     "\n",
     "Betweenness centrality of a node 𝑣 is the sum of the fraction of all-pairs shortest paths that pass through 𝑣\n",
     "\n",
-    "$C_{betweenness}=\\sum_{s \\neq v \\neq t} \\frac{\\sigma_{st}(v)}{\\sigma_{st}}$\n",
+    "$C_{betweenness}(v)=\\sum_{s \\neq v \\neq t} \\frac{\\sigma_{st}(v)}{\\sigma_{st}}$\n",
     "\n",
     "To speedup runtime of betweenness centrailty, the metric can be computed on a limited number of nodes (randomly selected) and then used to estimate the other scores.  For this example, the graphs are relatively small (under 5,000 nodes) so betweenness on every node will be computed.\n",
     "\n",
+    "See:\n",
+    "* [Betweenness Centrality on Wikipedia](https://en.wikipedia.org/wiki/Betweenness_centrality) for more details on the algorithm.\n",
+    "* [Learn more about Betweenness Centrality](https://www.sci.unich.it/~francesc/teaching/network/betweeness.html)\n",
+    "\n",
     "__Katz Centrality__ <br>\n",
     "Katz is a variant of degree centrality and of eigenvector centrality. \n",
     "Katz centrality is a measure of the relative importance of a node within the graph based on measuring the influence across the total number of walks between vertex pairs.\n",
@@ -60,7 +72,7 @@
     "\n",
     "\n",
     "See:\n",
-    "* [Katz on Wikipedia](https://en.wikipedia.org/wiki/Katz_centrality) for more details on the algorithm.\n",
+    "* [Katz Centrality on Wikipedia](https://en.wikipedia.org/wiki/Katz_centrality) for more details on the algorithm.\n",
     "* [Learn more about Katz Centrality](https://www.sci.unich.it/~francesc/teaching/network/katz.html)\n",
     "\n",
     "__Eigenvector Centrality__ <br>\n",

notebooks/algorithms/centrality/Degree.ipynb

@@ -27,7 +27,7 @@
     "\n",
     "See [Degree Centrality on Wikipedia](https://en.wikipedia.org/wiki/Degree_centrality) for more details on the algorithm.\n",
     "\n",
-    "$C_d(v) = \\frac{{\\text{{degree of vertex }} v}}{{\\text{{number of vertices in graph}} - 1}}$"
+    "$C_d(v) = \\frac{{\\text{degree of vertex } \\ v}}{{\\text{number of vertices in graph} - 1}}$"
    ]
   },
   {

notebooks/algorithms/centrality/Katz.ipynb

@@ -333,13 +333,6 @@
     "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n",
     "___"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

readme_pages/CONTRIBUTING.md

@@ -68,7 +68,7 @@ If you need more context on a particular issue, please ask.
 # So you want to contribute code
 
 **TL;DR General Development Process**
-1. Read the documentation on [building from source](./SOURCEBUILD.md) to learn how to setup, and validate, the development environment
+1. Read the documentation on [building from source](../docs/cugraph/source/installation/source_build.md) to learn how to setup, and validate, the development environment
 2. Read the RAPIDS [Code of Conduct](https://docs.rapids.ai/resources/conduct/)
 3. Find or submit an issue to work on (include a comment that you are working issue)
 4. Fork the cuGraph [repo](#fork) and Code (make sure to add unit tests)!
@@ -99,7 +99,7 @@ The RAPIDS cuGraph repo cannot directly be modified.  Contributions must come in
 ```git clone https://github.com/<YOUR GITHUB NAME>/cugraph.git```
 
 
-Read the section on [building cuGraph from source](./SOURCEBUILD.md) to validate that the environment is correct.
+Read the section on [building cuGraph from source](../docs/cugraph/source/installation/source_build.md) to validate that the environment is correct.
 
 **Pro Tip** add an upstream remote repository so that you can keep your forked repo in sync
 ```git remote add upstream https://github.com/rapidsai/cugraph.git```