Memgraph 3.0 release

next.config.mjs

@@ -3118,6 +3118,11 @@ export default withNextra({
         source: '/fundamentals/graph-modeling',
         destination: '/data-modeling',
         permanent: true
+      },
+      {
+        source: '/advanced-algorithms/available-algorithms#streaming-graph-algorithms',
+        destination: '/advanced-algorithms/available-algorithms#dynamic-graph-algorithms-enterprise',
+        permanent: true
       }
     ];
   },

pages/advanced-algorithms.mdx

@@ -21,6 +21,14 @@ paths](/advanced-algorithms/deep-path-traversal#all-shortest-paths) are
 built-in deep path traversal algorithms you can run using their specific
 clauses. 
 
+[Dynamic graph
+algorithms](/advanced-algorithms/available-algorithms#dynamic-graph-algorithms-enterprise)
+allow you to perform real-time, scalable computations on changing data. These
+algorithms have been specifically developed to handle high-speed updates and
+large-scale graph changes without needing to restart the computation process.
+Dynamic graph algorithms are a part of [Memgraph Enterprise
+license](/database-management/enabling-memgraph-enterprise). 
+
 For more algorithms, check the **Memgraph Advanced Graph Extensions** (**MAGE**)
 library. It is an [open-source repository](https://github.com/memgraph/mage)
 that contains [graph algorithms](/advanced-algorithms/available-algorithms)

pages/advanced-algorithms/available-algorithms.md → pages/advanced-algorithms/available-algorithms.mdx

@@ -3,6 +3,9 @@ title: Available advanced algorithms
 description: Learn how Memgraph's available algorithms revolutionize graph analysis. Get started with optimized algorithms for tailored queries and access detailed documentation with a snap.
 ---
 
+import { Callout } from 'nextra/components'
+
+
 # Available advanced algorithms
 
 If you require procedures designed to solve specific graph problems, there is a
@@ -48,7 +51,14 @@ library](/advanced-algorithms/install-mage).
 | [weakly_connected_components](/advanced-algorithms/available-algorithms/weakly_connected_components) | C++    | A module that finds weakly connected components in a graph.                                                                                                                                                                       |
 
 
-## Streaming graph algorithms
+## Dynamic graph algorithms (Enterprise)
+
+<Callout type="info">
+From Memgraph 3.0, dynamic graph algorithms became a part of the Memgraph Enterprise license. 
+To use dynamic graph algorithms, you need to [enable Memgraph Enterprise license](/database-management/enabling-memgraph-enterprise). 
+If you want to know more and learn how this affects you, read our [announcement](https://github.com/memgraph/memgraph/discussions/2615). 
+</Callout>
+
 
 | Algorithms                                                                                               | Lang   | Description                                                                                                                                             |
 |----------------------------------------------------------------------------------------------------------|--------|---------------------------------------------------------------------------------------------------------------------------------------------------------|

pages/advanced-algorithms/available-algorithms/betweenness_centrality_online.mdx

@@ -8,7 +8,13 @@ import { Callout } from 'nextra/components'
 import { Cards } from 'nextra/components'
 import GitHub from '/components/icons/GitHub'
 
-# betweenness_centrality_online
+# betweenness_centrality_online (Enterprise)
+
+<Callout type="info">
+From Memgraph 3.0, dynamic graph algorithms became a part of the Memgraph Enterprise license. 
+To use dynamic graph algorithms, you need to [enable Memgraph Enterprise license](/database-management/enabling-memgraph-enterprise). 
+If you want to know more and learn how this affects you, read our [announcement](https://github.com/memgraph/memgraph/discussions/2615). 
+</Callout>
 
 In network studies, centrality analysis illuminates the significance of nodes,
 with betweenness centrality being one of the most important metrics. It

pages/advanced-algorithms/available-algorithms/community_detection_online.mdx

@@ -8,7 +8,13 @@ import { Callout } from 'nextra/components'
 import { Cards } from 'nextra/components'
 import GitHub from '/components/icons/GitHub'
 
-# community_detection_online
+# community_detection_online (Enterprise)
+
+<Callout type="info">
+From Memgraph 3.0, dynamic graph algorithms became a part of the Memgraph Enterprise license. 
+To use dynamic graph algorithms, you need to [enable Memgraph Enterprise license](/database-management/enabling-memgraph-enterprise). 
+If you want to know more and learn how this affects you, read our [announcement](https://github.com/memgraph/memgraph/discussions/2615). 
+</Callout>
 
 Community in graphs mirrors real-world communities, like social circles. In a
 graph, communities are sets of nodes. M. Girvan and M. E. J. Newman note that
@@ -34,7 +40,7 @@ detection and can be applied to a diverse range of graphs.
   <Cards.Card
     icon={<GitHub />}
     title="Source code"
-    href="https://github.com/memgraph/mage/blob/main/cpp/community_detection_module/community_detection_online_module.cpp"
+    href="https://github.com/memgraph/memgraph/tree/master/query_modules/community_detection_module"
   />
 </Cards>
 

pages/advanced-algorithms/available-algorithms/katz_centrality_online.mdx

@@ -8,7 +8,14 @@ import { Callout } from 'nextra/components'
 import { Cards } from 'nextra/components'
 import GitHub from '/components/icons/GitHub'
 
-# katz_centrality_online
+# katz_centrality_online (Enterprise)
+
+<Callout type="info">
+From Memgraph 3.0, dynamic graph algorithms became a part of the Memgraph Enterprise license. 
+To use dynamic graph algorithms, you need to [enable Memgraph Enterprise license](/database-management/enabling-memgraph-enterprise). 
+If you want to know more and learn how this affects you, read our [announcement](https://github.com/memgraph/memgraph/discussions/2615). 
+</Callout>
+
 
 Because of its simplicity, **Katz Centrality** has become one of the most
 established centrality measurements. The definition of Katz centrality is that
@@ -51,7 +58,7 @@ Finally, the `reset()` function resets the context and enables new runs.
   <Cards.Card
     icon={<GitHub />}
     title="Source code"
-    href="https://github.com/memgraph/mage/blob/main/cpp/katz_centrality_module/katz_centrality_online_module.cpp"
+    href="https://github.com/memgraph/memgraph/tree/master/query_modules/katz_centrality_module"
   />
 </Cards>
 

pages/advanced-algorithms/available-algorithms/node2vec_online.mdx

@@ -8,7 +8,14 @@ import { Steps } from 'nextra/components'
 import { Cards } from 'nextra/components'
 import GitHub from '/components/icons/GitHub'
 
-# node2vec_online
+# node2vec_online (Enterprise)
+
+<Callout type="info">
+From Memgraph 3.0, dynamic graph algorithms became a part of the Memgraph Enterprise license. 
+To use dynamic graph algorithms, you need to [enable Memgraph Enterprise license](/database-management/enabling-memgraph-enterprise). 
+If you want to know more and learn how this affects you, read our [announcement](https://github.com/memgraph/memgraph/discussions/2615). 
+</Callout>
+
 
 The **node2vec_online** algorithm learns and updates temporal node embeddings on
 the fly for tracking and measuring node similarity over time in graph streams.
@@ -64,7 +71,7 @@ Ferenc Béres, Róbert Pálovics, Domokos Miklós Kelen and András A. Benczúr.
   <Cards.Card
     icon={<GitHub />}
     title="Source code"
-    href="https://github.com/memgraph/mage/blob/main/python/node2vec_online.py"
+    href="https://github.com/memgraph/memgraph/tree/master/query_modules/node2vec_online_module"
   />
 </Cards>
 

pages/advanced-algorithms/available-algorithms/pagerank_online.mdx

@@ -7,9 +7,16 @@ import { Steps } from 'nextra/components'
 import { Callout } from 'nextra/components'
 import GitHub from '/components/icons/GitHub'
 
-# pagerank_online
+# pagerank_online (Enterprise)
 
-**Online PageRank** is a streaming algorithm made for calculating
+<Callout type="info">
+From Memgraph 3.0, dynamic graph algorithms became a part of the Memgraph Enterprise license. 
+To use dynamic graph algorithms, you need to [enable Memgraph Enterprise license](/database-management/enabling-memgraph-enterprise). 
+If you want to know more and learn how this affects you, read our [announcement](https://github.com/memgraph/memgraph/discussions/2615). 
+</Callout>
+
+
+**Online PageRank** is a dynamic algorithm made for calculating
 [PageRank](/advanced-algorithms/available-algorithms/pagerank) in a graph
 streaming scenario. Incremental local changes are introduced in the algorithm
 to prevent users from recalculating PageRank values each time a change occurs in
@@ -37,7 +44,7 @@ Where `X_v` is the number of walks where the node `v` appears. The theorem
 written in the paper explains that RankApprox(v) is sharply concentrated around
 its expectation, which is Rank(v).
 
-[![docs-source](https://img.shields.io/badge/source-pagerank_online-FB6E00?logo=github&style=for-the-badge)](https://github.com/memgraph/mage/blob/main/cpp/pagerank_module/pagerank_online_module.cpp)
+[![docs-source](https://img.shields.io/badge/source-pagerank_online-FB6E00?logo=github&style=for-the-badge)](https://github.com/memgraph/memgraph/tree/master/query_modules/pagerank_module)
 
 | Trait               | Value                                                 |
 | ------------------- | ----------------------------------------------------- |