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Community notebook updates structure/testing/improvement (rapidsai#2397)
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Related to epic rapidsai#1405

Authors:
  - Don Acosta (https://github.com/acostadon)

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

URL: rapidsai#2397
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@acostadon
acostadon authored Jul 25, 2022
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12 changes: 6 additions & 6 deletions notebooks/README.md
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Expand Up @@ -16,12 +16,12 @@ This repository contains a collection of Jupyter Notebooks that outline how to r
| | [Degree](algorithms/centrality/Degree.ipynb) | Compute Degree Centraility for each vertex |
| | [Eigenvector](algorithms/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 |
| | [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 |
| | [Louvain](algorithms/community/Louvain.ipynb) and Leiden | Identify clusters in a graph using both the Louvain and Leiden algorithms |
| | [ECG](algorithms/community/ECG.ipynb) | Identify clusters in a graph using the Ensemble Clustering for Graph |
| | [K-Truss](algorithms/community/ktruss.ipynb) | Extracts the K-Truss cluster |
| | [Spectral-Clustering](algorithms/community/Spectral-Clustering.ipynb) | Identify clusters in a graph using Spectral Clustering with both<br> - Balanced Cut<br> - Modularity Modularity |
| | [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 |
| Core | | |
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5 changes: 2 additions & 3 deletions notebooks/algorithms/README.md
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Expand Up @@ -16,15 +16,14 @@ This repository contains a collection of Jupyter Notebooks that outline how to r
| | [Betweenness](centrality/Betweenness.ipynb) | Compute both Edge and Vertex Betweenness centrality |
| | [Degree](centrality/Degree.ipynb) | Compute Degree Centraility for each vertex |
| | [Eigenvector](centrality/Eigenvector.ipynb) | Compute Eigenvector for every vertex |

<!-- | Community | | |
| Community | | |
| | [Louvain](community/Louvain.ipynb) and Leiden | 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 |
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10 changes: 5 additions & 5 deletions notebooks/algorithms/centrality/README.md
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Expand Up @@ -15,11 +15,11 @@ But which vertices are most important? The answer depends on which measure/algor

|Algorithm |Notebooks Containing |Description |
| --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
|Degree Centrality| [Centrality](centrality/Centrality.ipynb), [Degree](centrality/Degree.ipynb) |Measure based on counting direct connections for each vertex|
|Betweenness Centrality| [Centrality](centrality/Centrality.ipynb), [Betweenness](centrality/Betweenness.ipynb) |Number of shortest paths through the vertex|
|Eigenvector Centrality|[Centrality](centrality/Centrality.ipynb), [Eigenvector](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|[Centrality](centrality/Centrality.ipynb), [Katz](centrality/Katz.ipynb) |Similar to Eigenvector but has tweaks to measure more weakly connected graph |
|Pagerank|[Centrality](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| [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|
|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. |

[System Requirements](../../README.md#requirements)

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