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Community notebook updates structure/testing/improvement #2397

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merged 20 commits into from
Jul 25, 2022
Merged

Community notebook updates structure/testing/improvement #2397

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e982b2c
fixed formatting
acostadon Jul 11, 2022
396f459
Merge remote-tracking branch 'upstream/branch-22.08' into branch-22.0…
acostadon Jul 11, 2022
76d231f
removed output
acostadon Jul 11, 2022
924ff2d
fixed a couple file path issues
acostadon Jul 11, 2022
c35c97c
tuning community readme, found a problem in algorithm readme
acostadon Jul 12, 2022
5793694
more clean-up, first copy of community clustering comparison.
acostadon Jul 12, 2022
72507cf
finished clustering comparisons
acostadon Jul 13, 2022
80f8274
updated triangles to use experimental cugraph implementation and more…
acostadon Jul 15, 2022
66df75f
Merge remote-tracking branch 'upstream/branch-22.08' into branch-22.0…
acostadon Jul 15, 2022
3b7ec9e
removed output from notebook
acostadon Jul 15, 2022
881a10f
formatted the comparison data
acostadon Jul 15, 2022
d3a1d4e
removed note about max vertices and fixed links in notebook level readme
acostadon Jul 18, 2022
d6c8eb7
cleaned up documentation
acostadon Jul 18, 2022
be61e6b
added new cluster diagram
acostadon Jul 19, 2022
48af5e4
fixed algro compare function and added new clustering diagram
acostadon Jul 19, 2022
66b76db
improved description of algorithms
acostadon Jul 19, 2022
db110eb
responded to review comments, moved matrix key and added some algo de…
acostadon Jul 20, 2022
00f657b
Merge remote-tracking branch 'upstream/branch-22.08' into branch-22.0…
acostadon Jul 20, 2022
7ac8932
responded to review comments
acostadon Jul 20, 2022
be02a40
fixed typo
acostadon Jul 20, 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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