Forward-merge branch-25.02 into branch-25.04

docs/cugraph-docs/source/conf.py

@@ -43,11 +43,13 @@
 extensions = [
     "breathe",
     "sphinx.ext.intersphinx",
+    "sphinxcontrib.jsmath",
     "sphinx.ext.autodoc",
     "sphinx.ext.autosummary",
     "numpydoc",
     "sphinx_markdown_tables",
     'sphinx.ext.doctest',
+    "sphinx.ext.mathjax",
     'sphinx.ext.linkcode',
     "IPython.sphinxext.ipython_console_highlighting",
     "IPython.sphinxext.ipython_directive",
@@ -56,6 +58,8 @@
     "sphinx_copybutton",
 ]
 
+jsmath_path = "https://cdn.jsdelivr.net/npm/mathjax@2/MathJax.js?config=TeX-AMS_HTML"
+
 
 ipython_mplbackend = 'str'
 

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

@@ -32,7 +32,7 @@ But which vertices are most important? The answer depends on which measure/algor
 
 ## 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/betweenness_centrality.md

@@ -6,8 +6,12 @@ See [Betweenness on Wikipedia](https://en.wikipedia.org/wiki/Betweenness_central
 
 Betweenness centrality of a node 𝑣 is the sum of the fraction of all-pairs shortest paths that pass through 𝑣
 
-$c_B(v) =\sum_{s,t \in V} \frac{\sigma(s, t|v)}{\sigma(s, t)}$
 
+<div class="math">
+\[
+c_B(v) =\sum_{s,t \in V} \frac{\sigma(s, t|v)}{\sigma(s, t)}
+\]
+</div>
 
 ## When to use BC
 Betweenness centrality is often used to answer questions like:
@@ -34,7 +38,7 @@ While cuGraph's parallelism migigates run time, [Big O notation](https://en.wiki
 Coming Soon
 
 ___
-Copyright (c) 2023, NVIDIA CORPORATION.
+Copyright (c) 2023-2025, 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

@@ -5,7 +5,12 @@ See [Degree Centrality on Wikipedia](https://en.wikipedia.org/wiki/Degree_centra
 
 Degree centrality of a vertex 𝑣 is the sum of the edges incident on that node.
 
-<img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/62c50cbf5f6cbe0842fe58fca63deb0f0772a829" />
+
+<div class="math">
+\[
+C_d(v) = \\frac{{\\text{degree of vertex } \\ v}}{{\\text{number of vertices in graph} - 1}}
+\]
+</div>
 
 ## When to use Degree Centrality
 * When you need a really quick identifcation of important nodes on very simply structured data.
@@ -24,7 +29,7 @@ While cuGraph's parallelism migigates run time, [Big O notation](https://en.wiki
 
 The cost of Degree Centrality is O(n) where n is the number of nodes.
 ___
-Copyright (c) 2023, NVIDIA CORPORATION.
+Copyright (c) 2023-2025, 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/eigenvector_centrality.md

@@ -9,7 +9,12 @@ See [Eigenvector Centrality on Wikipedia](https://en.wikipedia.org/wiki/Eigenvec
 The eigenvector centrality for node i is the
 i-th element of the vector x defined by the eigenvector equation.
 
-<img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/51c506ef0f23db1086b00ff6e5da847ff53cf5e9" />
+<div class="math">
+\[
+x_v = \\frac{1}{\\lambda}{\\sum_{t \\in M(v)}}x_t = \\frac{1}{\\lambda}{\\sum_{t \\in V}{a_v,x_t}
+\]
+</div>
+
 
 
 Where M(v) is the adjacency list for the set of vertices(v) and λ is a constant.
@@ -33,7 +38,7 @@ While cuGraph's parallelism migigates run time, [Big O notation](https://en.wiki
 O(VE) where V is the number of vertices(nodes) and Eis the number of edges.
 
 ___
-Copyright (c) 2023, NVIDIA CORPORATION.
+Copyright (c) 2023-2025, 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/jaccard_similarity.md

@@ -4,7 +4,12 @@ The Jaccard similarity between two sets is defined as the ratio of the volume of
 
 The Jaccard Similarity can then be defined as
 
-Jaccard similarity coefficient = $\frac{|A \cap B|}{|A \cup B|}$
+
+<div class="math">
+\[
+S_j = \frac{|A \cap B|}{|A \cup B|}
+\]
+</div>
 
 In graphs, the sets refer to the set of connected nodes or neighborhood of nodes A and B.
 
@@ -26,7 +31,7 @@ While cuGraph's parallelism mitigates run cost, [Big O notation](https://en.wiki
 The cost of calculating the Jaccard Similarity for a graph is  O(d * n) where d is the average degree of the nodes and n is the number of nodes.
 
 ___
-Copyright (c) 2023, NVIDIA CORPORATION.
+Copyright (c) 2023-2025, 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/katz_centrality.md

@@ -2,7 +2,11 @@
 
 Katz centrality is a measure of the relative importance of a vertex within the graph based on measuring the influence across the total number of walks between vertex pairs. Katz is similar to Eigenvector centrality. The main difference is that Katz also takes into account indirect relationships. The Katz calculation includes a user-controlled attenuation variable that controls the weight of indirect relationships. Otherwise it shares many of the advantages and disadvantages of Eigenvector centrality.
 
-$C_{katz}(i) = \sum_{k=1}^{\infty} \sum_{j=1}^{n} \alpha ^k(A^k)_{ji}$
+<div class="math">
+\[
+C_{katz}(i) = \sum_{k=1}^{\infty} \sum_{j=1}^{n} \alpha ^k(A^k)_{ji}
+\]
+</div>
 
 See [Katz on Wikipedia](https://en.wikipedia.org/wiki/Katz_centrality) for more details on the algorithm.
 
@@ -20,7 +24,7 @@ See [Katz on Wikipedia](https://en.wikipedia.org/wiki/Katz_centrality) for more
 Katz centraility has several stages with costs that add up as the graph gets larger. The overall cost is often O(n<sup>2</sup>) to O(n<sup>3</sup>) where n is the number of nodes.
 
 ___
-Copyright (c) 2023, NVIDIA CORPORATION.
+Copyright (c) 2023-2025, 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/overlap_similarity.md

@@ -3,7 +3,11 @@
 The Overlap Coefficient, also known as th Szymkiewicz–Simpson coefficient, between two sets is defined as the ratio of the volume of their intersection divided by the volume of the smaller set.
 The Overlap Coefficient can be defined as
 
-$overlap(A,B) = \frac{|A \cap B|}{min(|A|,|B|)}$
+<div class="math">
+\[
+overlap(A,B) = \frac{|A \cap B|}{min(|A|,|B|)}$\
+\]
+</div>
 
 [Learn more about Overlap Similarity](https://en.wikipedia.org/wiki/Overlap_coefficient)
 
@@ -24,7 +28,7 @@ While cuGraph's parallelism migigates run time, [Big O notation](https://en.wiki
 The cost to compute overlap similarity is O(n*d) where n is the number of nodes and d is the average degree of the nodes.
 
 ___
-Copyright (c) 2023, NVIDIA CORPORATION.
+Copyright (c) 2023-2025, 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/sorensen_coefficient.md

@@ -4,8 +4,13 @@ The Sørensen Coefficient, also called the Sørensen-Dice similarity coefficient
 
 It is defined as two times the size of the set intersection divided by the sum of the size of the two sets. The value ranges from 0 to 1.
 
-Sørensen coefficient = $\left(2 * |A \cap B| \right) \over \left(|A| + |B| \right)$
+Sørensen coefficient 
 
+<div class="math">
+\[
+\left(2 * |A \cap B| \right) \over \left(|A| + |B| \right)
+\]
+</div>
 
 In graphs, the sets refer to the set of connected nodes or neighborhood of nodes A and B.
 
@@ -26,7 +31,7 @@ While cuGraph's parallelism mitigates run cost, [Big O notation](https://en.wiki
 The cost to run O(n * m) where n is the number of nodes in the graph and m is the number of groups to test.
 
 ___
-Copyright (c) 2023, NVIDIA CORPORATION.
+Copyright (c) 2023-2025, 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