fixed formulas and paths and corrected a typo

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

@@ -34,7 +34,7 @@ Note: Multi-GPU, or MG, includes support for Multi-Node Multi-GPU (also called M
 |                   | [Ensemble Clustering for Graphs](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ECG.ipynb)            | Single-GPU     |  MG planned for 24.02 |
 |                   | [Spectral-Clustering - Balanced Cut](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Spectral-Clustering.ipynb) | Single-GPU     |      |
 |                   | [Spectral-Clustering - Modularity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Spectral-Clustering.ipynb)   | Single-GPU          |        |
-|                   | [Subgraph Extraction](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Subgraph-Extraction.ipyn)                | Single-GPU          |        |
+|                   | [Subgraph Extraction](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Subgraph-Extraction.ipynb)                | Single-GPU          |        |
 |                   | [Triangle Counting](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Triangle-Counting.ipynb)           | __Multi-GPU__ |           |
 |                   | [K-Truss](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ktruss.ipynb)                                | Single-GPU    |  MG planned for 2024  |
 | Components        |          |                |          |

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

@@ -15,20 +15,12 @@ But which vertices are most important? The answer depends on which measure/algor
 
 |Algorithm          |Notebooks Containing                                                     |Description                                                  |
 | --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
-|[Degree Centrality](./degree_centrality.html)| [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.html)| [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.html)|[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.html)|[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  |
+|[Degree Centrality](../degree_centrality.html)| [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.html)| [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.html)|[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.html)|[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| [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)
-
-
-
-| Author Credit |    Date    |  Update          | cuGraph Version |  Test Hardware |
-| --------------|------------|------------------|-----------------|----------------|
-| Brad Rees     | 04/19/2021 | created          | 0.19            | GV100, CUDA 11.0
-| Don Acosta    | 07/05/2022 | tested / updated | 22.08 nightly   | DGX Tesla V100 CUDA 11.5
 
 ## Copyright
 

docs/cugraph-docs/source/graph_support/algorithms/Similarity.md

@@ -15,22 +15,15 @@ Manipulation of the data before or after the graph analytic is not covered here.
 
 |Algorithm          |Notebooks Containing                                                     |Description                                                  |
 | --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
-|[Jaccard Smiliarity](./jaccard_similarity.html)| [Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb)                 ||
-|[Overlap Similarity](./overlap_similarity.html)| [Overlap Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Overlap-Similarity.ipynb)                    ||
-|[Sorensen](./sorensen_coefficient.html)|[Sorensen Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Sorensen_coefficient.ipynb)||
+|[Jaccard Smiliarity](../jaccard_similarity.html)| [Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb)                 ||
+|[Overlap Similarity](../overlap_similarity.html)| [Overlap Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Overlap-Similarity.ipynb)                    ||
+|[Sorensen](../sorensen_coefficient.html)|[Sorensen Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Sorensen_coefficient.ipynb)||
 |Personal Pagerank|[Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb)                 ||
 
 
-[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    | 07/05/2022 | tested / updated | 22.08 nightly   | DGX Tesla V100 CUDA 11.5
-
 ## Copyright
 
-Copyright (c) 2019 - 2023, NVIDIA CORPORATION.
+Copyright (c) 2019 - 2025, NVIDIA CORPORATION.
 
 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
 

docs/cugraph-docs/source/graph_support/algorithms/degree_centrality.md

@@ -8,7 +8,7 @@ Degree centrality of a vertex 𝑣 is the sum of the edges incident on that node
 
 <div class="math">
 \[
-C_d(v) = \\frac{{\\text{degree of vertex } \\ v}}{{\\text{number of vertices in graph} - 1}}
+C_d(v) = \frac{{\text{degree of vertex } \ v}}{{\text{number of vertices in graph} - 1}}
 \]
 </div>
 

docs/cugraph-docs/source/graph_support/algorithms/eigenvector_centrality.md

@@ -11,7 +11,7 @@ i-th element of the vector x defined by the eigenvector equation.
 
 <div class="math">
 \[
-x_v = \\frac{1}{\\lambda}{\\sum_{t \\in M(v)}}x_t = \\frac{1}{\\lambda}{\\sum_{t \\in V}{a_v,x_t}
+x_v = \frac{1}{\lambda}{\sum_{t \in M(v)}}x_t = \frac{1}{\lambda}{\sum_{t \in V}{a_v,x_t}
 \]
 </div>