diff --git a/notebooks/README.md b/notebooks/README.md
index d17edb8f129..56eb1d5c317 100644
--- a/notebooks/README.md
+++ b/notebooks/README.md
@@ -23,10 +23,12 @@ This repository contains a collection of Jupyter Notebooks that outline how to r
 |                 | [Subgraph Extraction](algorithms/community/Subgraph-Extraction.ipynb)   | Compute a subgraph of the existing graph including only the specified vertices |
 |                 | [Triangle Counting](algorithms/community/Triangle-Counting.ipynb)       | Count the number of Triangle in a graph                      |
 | Components      |                                                              |                                                              |
-|                 | [Connected Components](components/ConnectedComponents.ipynb) | Find weakly and strongly connected components in a graph     |
+|                 | [Connected Components](algorithms/components/ConnectedComponents.ipynb) | Find weakly and strongly connected components in a graph     |
 | Core            |                                                              |                                                              |
-|                 | [K-Core](cores/kcore.ipynb)                                  | Extracts the K-core cluster                                  |
-|                 | [Core Number](cores/core-number.ipynb)                       | Computer the Core number for each vertex in a graph          |
+|                 | [K-Core](algorithms/cores/kcore.ipynb)                                  | Extracts the K-core cluster                                  |
+|                 | [Core Number](algorithms/cores/core-number.ipynb)                       | Computer the Core number for each vertex in a graph          |
+Layout            |                                                              |                                                              |
+|                 | [Force-Atlas2](algorithms/layout/Force-Atlas2.ipynb)   |A large graph visualization achieved with cuGraph. |
 | Link Analysis   |                                                              |                                                              |
 |                 | [Pagerank](link_analysis/Pagerank.ipynb)                     | Compute the PageRank of every vertex in a graph              |
 |                 | [HITS](link_analysis/HITS.ipynb)                             | Compute the HITS' Hub and Authority scores for every vertex in a graph              |
diff --git a/notebooks/algorithms/README.md b/notebooks/algorithms/README.md
index bf0d6a3b27a..03262336fbc 100644
--- a/notebooks/algorithms/README.md
+++ b/notebooks/algorithms/README.md
@@ -17,18 +17,20 @@ This repository contains a collection of Jupyter Notebooks that outline how to r
 |                 | [Degree](centrality/Degree.ipynb)                            | Compute Degree Centraility for each vertex                   |
 |                 | [Eigenvector](centrality/Eigenvector.ipynb)                  | Compute Eigenvector for every vertex                         |
 | Community       |                                                              |                                                              |
-|                 | [Louvain](community/Louvain.ipynb) and Leiden                          | Identify clusters in a graph using both the Louvain and Leiden algorithms     |
+|                 | [Louvain](community/Louvain.ipynb)                           | Identify clusters in a graph using both the Louvain and Leiden algorithms     |
 |                 | [ECG](community/ECG.ipynb)                                   | Identify clusters in a graph using the Ensemble Clustering for Graph |
 |                 | [K-Truss](community/ktruss.ipynb)                                | Extracts the K-Truss cluster                                 |
 |                 | [Spectral-Clustering](community/Spectral-Clustering.ipynb)   | Identify clusters in a  graph using Spectral Clustering with both<br> - Balanced Cut<br> - Modularity Modularity |
 |                 | [Subgraph Extraction](community/Subgraph-Extraction.ipynb)   | Compute a subgraph of the existing graph including only the specified vertices |
 |                 | [Triangle Counting](community/Triangle-Counting.ipynb)       | Count the number of Triangle in a graph                      |
-<!--| Components      |                                                              |                                                              |
+Components      |                                                              |                                                              |
 |                 | [Connected Components](components/ConnectedComponents.ipynb) | Find weakly and strongly connected components in a graph     |
-| Core            |                                                              |                                                              |
-|                 | [K-Core](cores/kcore.ipynb)                                  | Extracts the K-core cluster                                  |
-|                 | [Core Number](cores/core-number.ipynb)                       | Computer the Core number for each vertex in a graph          |
-| Link Analysis   |                                                              |                                                              |
+| Cores            |                                                              |                                                              |
+|                | [core-number](cores/Core-number.ipynb)   | Computes the core number for every vertex of a graph G. The core number of a vertex is a maximal subgraph that contains only that vertex and others of degree k or more. |
+|                | [kcore](cores/kcore.ipynb)               |Find the k-core of a graph which is a maximal subgraph that contains nodes of degree k or more.|
+Layout            |                                                              |                                                              |
+|                | [Force-Atlas2](layout/Force-Atlas2.ipynb)   |A large graph visualization achieved with cuGraph. |
+<!--| Link Analysis   |                                                              |                                                              |
 |                 | [Pagerank](link_analysis/Pagerank.ipynb)                     | Compute the PageRank of every vertex in a graph              |
 |                 | [HITS](link_analysis/HITS.ipynb)                             | Compute the HITS' Hub and Authority scores for every vertex in a graph              |
 | Link Prediction |                                                              |                                                              |
diff --git a/notebooks/algorithms/centrality/Centrality.ipynb b/notebooks/algorithms/centrality/Centrality.ipynb
index 4a584a7fb87..72909fd81dd 100644
--- a/notebooks/algorithms/centrality/Centrality.ipynb
+++ b/notebooks/algorithms/centrality/Centrality.ipynb
@@ -106,7 +106,7 @@
     "Anthropological Research 33, 452-473 (1977).*\n",
     "\n",
     "\n",
-    "<img src=\"../../img/zachary_black_lines.png\" width=\"35%\"/>\n",
+    "<img src=\"../../img/zachary_graph_comm.png\" width=\"35%\"/>\n",
     "\n",
     "Because the test data has vertex IDs starting at 1, the auto-renumber feature of cuGraph (mentioned above) will be used so the starting vertex ID is zero for maximum efficiency. The resulting data will then be auto-unrenumbered, making the entire renumbering process transparent to users."
    ]
@@ -457,7 +457,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.9.13"
   },
   "vscode": {
    "interpreter": {
diff --git a/notebooks/algorithms/centrality/README.md b/notebooks/algorithms/centrality/README.md
index 6f116094b15..f15ed7bdc04 100644
--- a/notebooks/algorithms/centrality/README.md
+++ b/notebooks/algorithms/centrality/README.md
@@ -15,8 +15,8 @@ But which vertices are most important? The answer depends on which measure/algor
 
 |Algorithm          |Notebooks Containing                                                     |Description                                                  |
 | --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
-|Degree Centrality| [Centrality](./Centrality.ipynb), [Degree](centrality/Degree.ipynb)                   |Measure based on counting direct connections for each vertex|
-|Betweenness Centrality| [Centrality](./Centrality.ipynb), [Betweenness](centrality/Betweenness.ipynb)                    |Number of shortest paths through the vertex|
+|Degree Centrality| [Centrality](./Centrality.ipynb), [Degree](./Degree.ipynb)                   |Measure based on counting direct connections for each vertex|
+|Betweenness Centrality| [Centrality](./Centrality.ipynb), [Betweenness](./Betweenness.ipynb)                    |Number of shortest paths through the vertex|
 |Eigenvector Centrality|[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|[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.  |
diff --git a/notebooks/components/ConnectedComponents.ipynb b/notebooks/algorithms/components/ConnectedComponents.ipynb
similarity index 89%
rename from notebooks/components/ConnectedComponents.ipynb
rename to notebooks/algorithms/components/ConnectedComponents.ipynb
index 8b8c26029d4..0259c314ccf 100755
--- a/notebooks/components/ConnectedComponents.ipynb
+++ b/notebooks/algorithms/components/ConnectedComponents.ipynb
@@ -16,16 +16,23 @@
     "\n",
     "_Notebook Credits_\n",
     "\n",
-    "| Author Credit |    Date    |  Update          | cuGraph Version |  Test Hardware     |\n",
-    "| --------------|------------|------------------|-----------------|--------------------|\n",
-    "| Kumar Aatish  | 08/13/2019 | created          | 0.15            | GV100, CUDA 10.2   |\n",
-    "| Brad Rees     | 10/18/2021 | updated          | 21.12 nightly   | GV100, CUDA 11.4   |\n",
+    "| Author Credit |    Date    |  Update          | cuGraph Version |  Test Hardware              |\n",
+    "| --------------|------------|------------------|-----------------|-----------------------------|\n",
+    "| Kumar Aatish  | 08/13/2019 | created          | 0.15            | GV100, CUDA 10.2            |\n",
+    "| Brad Rees     | 10/18/2021 | updated          | 21.12 nightly   | GV100, CUDA 11.4            |\n",
+    "| Don Acosta    | 07/22/2021 | updated          | 22.08 nightly   | DGX Tesla V100, CUDA 11.5   |\n",
     "\n",
     "\n",
     "\n",
     "\n",
     "## Introduction\n",
     "\n",
+    "### Additional Information:\n",
+    "* Strong component - directed graph - needs to have a directed path from every vertex to every other vertex\n",
+    "* Weak component - undirected - vertices need to be reachable from every other vertex\n",
+    "* Strong components are a subset of weak components \n",
+    "\n",
+    "\n",
     "### Weakly Connected Components\n",
     "To compute WCC for a graph in cuGraph we use:\n",
     "\n",
@@ -40,8 +47,7 @@
     "        as an edge list (edge weights are not used for this algorithm).\n",
     "        Currently, the graph should be undirected where an undirected edge is\n",
     "        represented by a directed edge in both directions. The adjacency list\n",
-    "        will be computed if not already present. The number of vertices should\n",
-    "        fit into a 32b int.\n",
+    "        will be computed if not already present.\n",
     "\n",
     "    Returns\n",
     "    -------\n",
@@ -87,7 +93,6 @@
    "metadata": {},
    "source": [
     "### Some notes about vertex IDs...\n",
-    "* The current version of cuGraph requires that vertex IDs be representable as 32-bit integers, meaning graphs currently can contain at most 2^32 unique vertex IDs. However, this limitation is being actively addressed and a version of cuGraph that accommodates more than 2^32 vertices will be available in the near future.\n",
     "* cuGraph will automatically renumber graphs to an internal format consisting of a contiguous series of integers starting from 0, and convert back to the original IDs when returning data to the caller. If the vertex IDs of the data are already a contiguous series of integers starting from 0, the auto-renumbering step can be skipped for faster graph creation times.\n",
     "  * To skip auto-renumbering, set the `renumber` boolean arg to `False` when calling the appropriate graph creation API (eg. `G.from_cudf_edgelist(gdf_r, source='src', destination='dst', renumber=False)`).\n",
     "  * For more advanced renumbering support, see the examples in `structure/renumber.ipynb` and `structure/renumber-2.ipynb`\n"
@@ -101,11 +106,11 @@
     "We will be using the Netscience dataset :  \n",
     "*M. E. J. Newman, Finding community structure in networks using the eigenvectors of matrices, Preprint physics/0605087 (2006)*\n",
     "\n",
-    "The graph netscience contains a coauthorship network of scientists working on network theory and experiment. The version given here contains all components of the network, for a total of 1589 scientists, with the the largest component of 379 scientists.\n",
+    "The graph netscience contains a co-authorship network of scientists working on network theory and experiments. The version given here contains all components of the network, for a total of 1589 scientists, with the the largest component of 379 scientists.\n",
     "\n",
     "Netscience Adjacency Matrix               |NetScience Strongly Connected Components\n",
     ":---------------------------------------------|------------------------------------------------------------:\n",
-    "![](../img/netscience.png \"Credit : https://www.cise.ufl.edu/research/sparse/matrices/Newman/netscience\") | ![](../img/netscience_scc.png \"Credit : https://www.cise.ufl.edu/research/sparse/matrices/Newman/netscience\")\n",
+    "![](../../img/netscience.png \"Credit : https://www.cise.ufl.edu/research/sparse/matrices/Newman/netscience\") | ![](../../img/netscience_scc.png \"Credit : https://www.cise.ufl.edu/research/sparse/matrices/Newman/netscience\")\n",
     "  \n",
     "Matrix plots above by Yifan Hu, AT&T Labs Visualization Group."
    ]
@@ -142,7 +147,7 @@
    "outputs": [],
    "source": [
     "# Test file\n",
-    "datafile='../data/netscience.csv'\n",
+    "datafile='../../data/netscience.csv'\n",
     "\n",
     "# the datafile contains three columns, but we only want to use the first two. \n",
     "# We will use the \"usecols' feature of read_csv to ignore that column\n",
@@ -346,7 +351,7 @@
    "metadata": {},
    "source": [
     "___\n",
-    "Copyright (c) 2019-2020, NVIDIA CORPORATION.\n",
+    "Copyright (c) 2019-2022, NVIDIA CORPORATION.\n",
     "\n",
     "Licensed under the Apache License, Version 2.0 (the \"License\");  you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n",
     "\n",
@@ -357,9 +362,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "cugraph_dev",
+   "display_name": "Python 3.8.13 ('cugraph_dev')",
    "language": "python",
-   "name": "cugraph_dev"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -371,7 +376,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "cee8a395f2f0c5a5bcf513ae8b620111f4346eff6dc64e1ea99c951b2ec68604"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/algorithms/components/README.md b/notebooks/algorithms/components/README.md
new file mode 100644
index 00000000000..e14e886a7e2
--- /dev/null
+++ b/notebooks/algorithms/components/README.md
@@ -0,0 +1,34 @@
+
+# cuGraph Components Algorithms
+
+<img src="../../img/zachary_black_lines.png" width="35%"/>
+
+cuGraph Components notebooks contain Jupyter Notebooks that demonstrate algorithms to identify the connected subgraphs within a graph.
+
+Manipulation of the data before or after the graph analytic is not covered here.   Extended, more problem focused, notebooks are being created and available https://github.com/rapidsai/notebooks-extended
+
+## Summary
+
+|Algorithm          |Notebooks Containing                                                     |Description                                                  |
+| --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
+|Weakly Connected Components   | [ConnectedComponents](ConnectedComponents.ipynb)   |Find the largest connected components in a graph. Considering directed paths or non-directed paths |
+|Strongly Connected Components | [ConnectedComponents](ConnectedComponents.ipynb)               |Find the connected components in a graph considering directed paths only|
+
+[System Requirements](../../README.md#requirements)
+
+| Author Credit |    Date    |  Update          | cuGraph Version |  Test Hardware |
+| --------------|------------|------------------|-----------------|----------------|
+| Brad Rees     | 04/19/2021 | created          | 0.19            | GV100, CUDA 11.0
+| Don Acosta    | 07/21/2022 | tested / updated | 22.08 nightly   | DGX Tesla V100 CUDA 11.5
+
+## Copyright
+
+Copyright (c) 2019-2022, NVIDIA CORPORATION.  All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");  you may not use this file except in compliance with the License.  You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0 
+
+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.
+
+![RAPIDS](../../img/rapids_logo.png)
diff --git a/notebooks/algorithms/cores/README.md b/notebooks/algorithms/cores/README.md
new file mode 100644
index 00000000000..e36677ffe32
--- /dev/null
+++ b/notebooks/algorithms/cores/README.md
@@ -0,0 +1,34 @@
+
+# cuGraph Core Algorithms
+
+<img src="../../img/zachary_black_lines.png" width="35%"/>
+
+cuGraph Cores notebooks contain Jupyter Notebooks that demonstrate algorithms to find maximally connected subgraphs within a graph. Either identifying the maximum k-core at the vertex (core-number) or graph  level (K-Cores).
+
+Manipulation of the data before or after the graph analytic is not covered here.   Extended, more problem focused, notebooks are being created and available https://github.com/rapidsai/notebooks-extended
+
+## Summary
+
+|Algorithm          |Notebooks Containing                                                     |Description                                                  |
+| --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
+|Core Number   | [core-number](core-number.ipynb)   | Computes the core number for every vertex of a graph G. The core number of a vertex is a maximal subgraph that contains only that vertex and others of degree k or more. |
+|K-Cores | [kcore](kcore.ipynb)               |Find the k-core of a graph which is a maximal subgraph that contains nodes of degree k or more.|
+
+[System Requirements](../../README.md#requirements)
+
+| Author Credit |    Date    |  Update          | cuGraph Version |  Test Hardware |
+| --------------|------------|------------------|-----------------|----------------|
+| Brad Rees     | 04/19/2021 | created          | 0.19            | GV100, CUDA 11.0
+| Don Acosta    | 07/21/2022 | tested / updated | 22.08 nightly   | DGX Tesla V100 CUDA 11.5
+
+## Copyright
+
+Copyright (c) 2019-2022, NVIDIA CORPORATION.  All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");  you may not use this file except in compliance with the License.  You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0 
+
+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.
+
+![RAPIDS](../../img/rapids_logo.png)
diff --git a/notebooks/cores/core-number.ipynb b/notebooks/algorithms/cores/core-number.ipynb
similarity index 80%
rename from notebooks/cores/core-number.ipynb
rename to notebooks/algorithms/cores/core-number.ipynb
index 6190f653020..64b8eada7ef 100755
--- a/notebooks/cores/core-number.ipynb
+++ b/notebooks/algorithms/cores/core-number.ipynb
@@ -9,21 +9,17 @@
     "\n",
     "In this notebook, we will use cuGraph to compute the core number of every vertex in our test graph  \n",
     "\n",
-    "Notebook Credits\n",
-    "* Original Authors: Bradley Rees\n",
-    "* Created:   10/28/2019\n",
-    "* Last Edit: 08/16/2020\n",
-    "\n",
-    "RAPIDS Versions: 0.13\n",
-    "\n",
-    "Test Hardware\n",
-    "* GV100 32G, CUDA 10.2\n",
     "\n",
+    "_Notebook Credits_\n",
     "\n",
+    "| Author Credit |    Date    |  Update          | cuGraph Version |  Test Hardware     |\n",
+    "| --------------|------------|------------------|-----------------|--------------------|\n",
+    "| Brad Rees     | 10/28/2019 | created          | 0.13            | GV100, CUDA 10.2   |\n",
+    "| Don Acosta    | 07/21/2022 | updated/tested   | 22.08 nightly   | DGX Tesla V100, CUDA 11.5   |\n",
     "\n",
     "## Introduction\n",
     "\n",
-    "Core Number computes the core number for every vertex of a graph G. A k-core of a graph is a maximal subgraph that contains nodes of degree k or more. A node has a core number of k if it belongs to a k-core but not to k+1-core.  This call does not support a graph with self-loops and parallel edges.\n",
+    "Core Number is computed for every vertex of a graph G. A k-core of a graph is a maximal connected subgraph that contains vertices of degree k or more. A vertex has a core number of k if it belongs to a k-core but not to k+1-core.  This call does not support a graph with self-loops and parallel edges.\n",
     "\n",
     "For a detailed description of the algorithm see: https://en.wikipedia.org/wiki/Degeneracy_(graph_theory)\n",
     "\n",
@@ -48,7 +44,6 @@
    "metadata": {},
    "source": [
     "### Some notes about vertex IDs...\n",
-    "* The current version of cuGraph requires that vertex IDs be representable as 32-bit integers, meaning graphs currently can contain at most 2^32 unique vertex IDs. However, this limitation is being actively addressed and a version of cuGraph that accommodates more than 2^32 vertices will be available in the near future.\n",
     "* cuGraph will automatically renumber graphs to an internal format consisting of a contiguous series of integers starting from 0, and convert back to the original IDs when returning data to the caller. If the vertex IDs of the data are already a contiguous series of integers starting from 0, the auto-renumbering step can be skipped for faster graph creation times.\n",
     "  * To skip auto-renumbering, set the `renumber` boolean arg to `False` when calling the appropriate graph creation API (eg. `G.from_cudf_edgelist(gdf_r, source='src', destination='dst', renumber=False)`).\n",
     "  * For more advanced renumbering support, see the examples in `structure/renumber.ipynb` and `structure/renumber-2.ipynb`\n"
@@ -64,7 +59,7 @@
     "Anthropological Research 33, 452-473 (1977).*\n",
     "\n",
     "\n",
-    "![Karate Club](../img/zachary_black_lines.png)\n"
+    "<img src=\"../../img/zachary_black_lines.png\" width=\"35%\"/>\n"
    ]
   },
   {
@@ -99,7 +94,7 @@
    "outputs": [],
    "source": [
     "# Test file    \n",
-    "datafile='../data//karate-data.csv'"
+    "datafile='../../data/karate-data.csv'"
    ]
   },
   {
@@ -154,7 +149,7 @@
    "metadata": {},
    "source": [
     "___\n",
-    "Copyright (c) 2019-2020, NVIDIA CORPORATION.\n",
+    "Copyright (c) 2019-2022, NVIDIA CORPORATION.\n",
     "\n",
     "Licensed under the Apache License, Version 2.0 (the \"License\");  you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n",
     "\n",
@@ -165,9 +160,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "cugraph_dev",
+   "display_name": "Python 3.8.13 ('cugraph_dev')",
    "language": "python",
-   "name": "cugraph_dev"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -179,7 +174,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.9.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "cee8a395f2f0c5a5bcf513ae8b620111f4346eff6dc64e1ea99c951b2ec68604"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/cores/kcore.ipynb b/notebooks/algorithms/cores/kcore.ipynb
similarity index 87%
rename from notebooks/cores/kcore.ipynb
rename to notebooks/algorithms/cores/kcore.ipynb
index c19f46dc8fe..432a46834a1 100755
--- a/notebooks/cores/kcore.ipynb
+++ b/notebooks/algorithms/cores/kcore.ipynb
@@ -9,17 +9,14 @@
     "\n",
     "In this notebook, we will use cuGraph to identify the K-Cores clusters in a test graph  \n",
     "\n",
-    "Notebook Credits\n",
-    "* Original Authors: Bradley Rees\n",
-    "* Created:   10/28/2019\n",
-    "* Last Edit: 08/16/2020\n",
-    "\n",
-    "RAPIDS Versions: 0.13\n",
-    "\n",
-    "Test Hardware\n",
-    "* GV100 32G, CUDA 10.2\n",
     "\n",
+    "_Notebook Credits_\n",
     "\n",
+    "| Author Credit |    Date    |  Update          | cuGraph Version |  Test Hardware     |\n",
+    "| --------------|------------|------------------|-----------------|--------------------|\n",
+    "| Brad Rees     | 10/28/2019 | created          | 0.13            | GV100, CUDA 10.2   |\n",
+    "| Brad Rees     | 08/16/2020 | created          | 0.15            | GV100, CUDA 10.2   |\n",
+    "| Don Acosta    | 07/21/2022 | updated/tested   | 22.08 nightly   | DGX Tesla V100, CUDA 11.5   |\n",
     "\n",
     "## Introduction\n",
     "\n",
@@ -32,11 +29,11 @@
     "* __gc = cugraph.k_core(G, k=None, core_number=None)__\n",
     "    * G: A cugraph.Graph object\n",
     "    * k: optional, The _k_ value to use\n",
-    "    * core_number: optional, Precomputed core number of the nodes of the graph G.  The see noptebook on Core-Number\n",
+    "    * core_number: optional, Precomputed core number of the nodes of the graph G.  The see notebook on Core-Number\n",
     "    \n",
     "Returns:\n",
     "* __gc__\n",
-    "    * A new graph the contains the K-Core\n",
+    "    * A new graph that contains the K-Core\n",
     "\n",
     "All vertices with the same partition ID are in the same cluster\n",
     "\n"
@@ -47,7 +44,6 @@
    "metadata": {},
    "source": [
     "### Some notes about vertex IDs...\n",
-    "* The current version of cuGraph requires that vertex IDs be representable as 32-bit integers, meaning graphs currently can contain at most 2^32 unique vertex IDs. However, this limitation is being actively addressed and a version of cuGraph that accommodates more than 2^32 vertices will be available in the near future.\n",
     "* cuGraph will automatically renumber graphs to an internal format consisting of a contiguous series of integers starting from 0, and convert back to the original IDs when returning data to the caller. If the vertex IDs of the data are already a contiguous series of integers starting from 0, the auto-renumbering step can be skipped for faster graph creation times.\n",
     "  * To skip auto-renumbering, set the `renumber` boolean arg to `False` when calling the appropriate graph creation API (eg. `G.from_cudf_edgelist(gdf_r, source='src', destination='dst', renumber=False)`).\n",
     "  * For more advanced renumbering support, see the examples in `structure/renumber.ipynb` and `structure/renumber-2.ipynb`\n"
@@ -63,7 +59,7 @@
     "Anthropological Research 33, 452-473 (1977).*\n",
     "\n",
     "\n",
-    "![Karate Club](../img/zachary_black_lines.png)\n"
+    "<img src=\"../../img/zachary_black_lines.png\" width=\"35%\"/>\n"
    ]
   },
   {
@@ -98,7 +94,7 @@
    "outputs": [],
    "source": [
     "# Test file    \n",
-    "datafile='../data//karate-data.csv'"
+    "datafile='../../data/karate-data.csv'"
    ]
   },
   {
@@ -192,7 +188,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![Karate Club](../img/karete-kcore.jpg)"
+    "![Karate Club](../../img/karete-kcore.jpg)"
    ]
   },
   {
@@ -260,7 +256,7 @@
    "metadata": {},
    "source": [
     "___\n",
-    "Copyright (c) 2019-2020, NVIDIA CORPORATION.\n",
+    "Copyright (c) 2019-2022, NVIDIA CORPORATION.\n",
     "\n",
     "Licensed under the Apache License, Version 2.0 (the \"License\");  you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n",
     "\n",
@@ -271,9 +267,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "cugraph_dev",
+   "display_name": "Python 3.8.13 ('cugraph_dev')",
    "language": "python",
-   "name": "cugraph_dev"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -285,7 +281,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.9.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "cee8a395f2f0c5a5bcf513ae8b620111f4346eff6dc64e1ea99c951b2ec68604"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/layout/Force-Atlas2.ipynb b/notebooks/algorithms/layout/Force-Atlas2.ipynb
similarity index 92%
rename from notebooks/layout/Force-Atlas2.ipynb
rename to notebooks/algorithms/layout/Force-Atlas2.ipynb
index df306e2aaf5..90c39294cca 100644
--- a/notebooks/layout/Force-Atlas2.ipynb
+++ b/notebooks/algorithms/layout/Force-Atlas2.ipynb
@@ -36,12 +36,15 @@
     "See https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0098679 for more details.\n",
     "\n",
     "\n",
-    "Please refer to the documentation https://docs.rapids.ai/api/cugraph/stable/api.html#module-cugraph.layout.force_atlas2 on how to use the different parameters."
+    "Please refer to the [documentation](https://docs.rapids.ai/api/cugraph/stable/api_docs/api/cugraph.force_atlas2.html)  on how to use the different parameters.\n",
+    "\n",
+    "This library has some additional dependencies that need to be installed prior to running:\n",
+    "* Holoviews - provides "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +95,7 @@
    "outputs": [],
    "source": [
     "# Define the path to the test data  \n",
-    "datafile = '../data/netscience.csv'"
+    "datafile = '../../data/netscience.csv'"
    ]
   },
   {
@@ -231,9 +234,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "cugraph_dev",
+   "display_name": "Python 3.8.13 ('cugraph_dev')",
    "language": "python",
-   "name": "cugraph_dev"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -245,7 +248,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "cee8a395f2f0c5a5bcf513ae8b620111f4346eff6dc64e1ea99c951b2ec68604"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/link_analysis/Pagerank.ipynb b/notebooks/link_analysis/Pagerank.ipynb
index 9e55c52ebc7..2ee8ca045c3 100755
--- a/notebooks/link_analysis/Pagerank.ipynb
+++ b/notebooks/link_analysis/Pagerank.ipynb
@@ -70,33 +70,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: networkx in /opt/conda/envs/rapids/lib/python3.9/site-packages (2.6.3)\n",
-      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
-      "\u001b[0mRequirement already satisfied: scipy in /opt/conda/envs/rapids/lib/python3.9/site-packages (1.6.0)\n",
-      "Requirement already satisfied: numpy>=1.16.5 in /opt/conda/envs/rapids/lib/python3.9/site-packages (from scipy) (1.21.5)\n",
-      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
-      "\u001b[0m"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# The notebook compares cuGraph to NetworkX,  \n",
     "# therefore there some additional non-RAPIDS python libraries need to be installed. \n",
     "# Please run this cell if you need the additional libraries\n",
-    "!pip install networkx\n",
-    "!pip install scipy"
+    "%pip install networkx\n",
+    "%pip install scipy"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -138,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,53 +163,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'1': 0.09701602879679112,\n",
-       " '2': 0.052883521402737124,\n",
-       " '3': 0.05707824474239911,\n",
-       " '4': 0.03586215186028655,\n",
-       " '5': 0.021982948286246903,\n",
-       " '6': 0.0291187010552997,\n",
-       " '7': 0.0291187010552997,\n",
-       " '8': 0.024490182143018978,\n",
-       " '9': 0.029762235160273865,\n",
-       " '11': 0.021982948286246907,\n",
-       " '12': 0.00956494892526498,\n",
-       " '13': 0.014645330577424495,\n",
-       " '14': 0.02953411440759793,\n",
-       " '18': 0.014558421109725602,\n",
-       " '20': 0.019602353374304556,\n",
-       " '22': 0.014558421109725602,\n",
-       " '32': 0.03715129664953499,\n",
-       " '31': 0.024585898152929064,\n",
-       " '10': 0.014307076351595165,\n",
-       " '28': 0.02563597770769009,\n",
-       " '29': 0.019571497200047343,\n",
-       " '33': 0.0716958652138451,\n",
-       " '17': 0.01678918865030142,\n",
-       " '34': 0.10092357454636647,\n",
-       " '15': 0.014531841309923067,\n",
-       " '16': 0.014531841309923067,\n",
-       " '19': 0.014531841309923067,\n",
-       " '21': 0.014531841309923067,\n",
-       " '23': 0.014531841309923067,\n",
-       " '24': 0.03151619714292456,\n",
-       " '26': 0.021005086910429464,\n",
-       " '30': 0.02628364427428044,\n",
-       " '25': 0.02107469969393673,\n",
-       " '27': 0.015041538663860599}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "pr_nx"
    ]
@@ -256,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -273,18 +216,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/envs/rapids/lib/python3.9/site-packages/cudf/core/indexed_frame.py:2271: FutureWarning: append is deprecated and will be removed in a future version. Use concat instead.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# create a Graph using the source (src) and destination (dst) vertex pairs from the Dataframe \n",
     "G = cugraph.from_edgelist(gdf, source='src', destination='dst')"
@@ -299,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -322,17 +256,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best vertex is 34 with score of 0.100917324\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Find the most important vertex using the scores\n",
     "# These methods should only be used for small graph\n",
@@ -356,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -366,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -380,17 +306,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Best vertex is 34 with score of 0.100917324\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print_pagerank_threshold(gdf_page, pr_max)"
    ]
@@ -408,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -417,66 +335,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>pagerank</th>\n",
-       "      <th>vertex</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>0.100917</td>\n",
-       "      <td>34</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>0.096999</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>0.071692</td>\n",
-       "      <td>33</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    pagerank  vertex\n",
-       "16  0.100917      34\n",
-       "17  0.096999       1\n",
-       "18  0.071692      33"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "sort_pr.head(3)"
    ]
@@ -491,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -500,70 +361,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>in_degree</th>\n",
-       "      <th>out_degree</th>\n",
-       "      <th>vertex</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>17</td>\n",
-       "      <td>17</td>\n",
-       "      <td>34</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>16</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>12</td>\n",
-       "      <td>12</td>\n",
-       "      <td>33</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    in_degree  out_degree  vertex\n",
-       "16         17          17      34\n",
-       "17         16          16       1\n",
-       "18         12          12      33"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "d.sort_values('out_degree', ascending=False).head(3)"
    ]
@@ -592,7 +392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -604,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -613,132 +413,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>pagerank</th>\n",
-       "      <th>vertex</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.121756</td>\n",
-       "      <td>26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0.085409</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>0.077714</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    pagerank  vertex\n",
-       "4   0.121756      26\n",
-       "10  0.085409      17\n",
-       "17  0.077714       1"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "ppr.sort_values('pagerank', ascending=False).head(3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>pagerank</th>\n",
-       "      <th>vertex</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.021006</td>\n",
-       "      <td>26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0.016785</td>\n",
-       "      <td>17</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>0.096999</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    pagerank  vertex\n",
-       "4   0.021006      26\n",
-       "10  0.016785      17\n",
-       "17  0.096999       1"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# looking at the initial PageRank values\n",
     "gdf_page[gdf_page['vertex'].isin([26,17,1])]"